https://wikibase.slis.ua.edu/w/api.php?action=feedcontributions&feedformat=atom&user=SmaccallWikibase.slis.ua.edu - User contributions [en]2026-04-11T04:34:55ZUser contributionsMediaWiki 1.34.2https://wikibase.slis.ua.edu/w/index.php?title=Publications_and_Presentations&diff=280012Publications and Presentations2025-12-05T17:25:53Z<p>Smaccall: </p>
<hr />
<div><b>Publications and Presentations of the [https://wikibase.slis.ua.edu/ Linked Data Research Group] at the University of Alabama SLIS.</b><br />
<br />
# Liu, Ho., Aderon, C., Wagon, N., Bamba, A. L., Li, X., Liu, Hu., MacCall, S. & Gan, Y. (2023). Automated player identification and indexing using two-stage deep learning network. Scientific Reports, 13, article number: 10036 (<b>available open access: [https://doi.org/10.1038/s41598-023-36657-5 https://doi.org/10.1038/s41598-023-36657-5]</b>).<br />
# Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[https://ir.ua.edu/handle/123456789/17433 presentation slides and speaker notes]</b>).<br />
# Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization.<br />
# Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology.<br />
# Nguyen Nguyen, Jeff Reidy, and Noah Wagnon (2021). "Final Presentation: Automated Jersey Number Recognition". Senior design team presentation for EECE 494.407 for 2020-21 academic year at The University of Alabama. <br />
# MacCall, S.L., & Liu, H. (2020). Data-driven semantic indexing of digital assets documenting American football game action. Poster presented at the 2020 annual meeting of the American Society for Information Science and Technology. [https://doi.org/10.1002/pra2.414 https://doi.org/10.1002/pra2.414]<br />
# MacCall, S.L., Bott, G., Anderson, C.M., & Liu, H. (2020). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase I: How much data can be recovered from Alabama football history? Presented at the University of Alabama 2020 Faculty Research Day, Tuscaloosa, AL. [https://tinyurl.com/yyr5exan Virtual session due to COVID19]<br />
# MacCall, S.L. (2020). Systems and methods for digital asset organization. U.S. Patent number 10,534,812. <br />
# MacCall, S.L., Liu, H., & Anderson, C.M. (2020). Statistical data recovery from historical documentation of Alabama football games using Wikibase as a repository. Interactive demonstration accepted for Connecting Collections as Data: Transforming Communities, Sharing Knowledge, and Building Networks with International GLAM Labs, Washington, DC. (Conference canceled due to COVID-19)<br />
# MacCall, S.L. (2020). Data-driven semantic DAM indexing incorporating statistical play-by-play game logs: A linked data application using Wikibase from the 2017 football season of the Alabama Crimson Tide. Conference paper presented at the 2020 LD4 Conference on Linked Data in Libraries, College Station, TX. ([https://tinyurl.com/y5wcxqen Video of presentation])<br />
# Anderson, C.M., Liu, H., & MacCall, S.L. (2020). Crowdsourcing in a semantic indexing workflow for efficiently organizing historical multimedia sports collections. Poster accepted for the 2020 Annual Meeting of the Alabama Library Association, Birmingham, AL. (Conference canceled due to COVID-19)<br />
# MacCall, S.L., Liu, H., & Anderson, C.M. (2019). How much statistical data can be recovered from Alabama football history? Piloting a crowdsourced approach using Wikibase as data repository. Conference paper presented at 2019 Digitorium Digital Humanities Conference, Tuscaloosa, AL. [UA Institutional Repository deposit: [https://ir.ua.edu/handle/123456789/6574 https://ir.ua.edu/handle/123456789/6574]]<br />
# MacCall, S.L. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 2: Developing SLIS research capacity for key linked open data technologies. University of Alabama School of Library and Information Studies Research Fund -$1,000. Funded. <br />
# MacCall, S.L., & Bott, G. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 1: How much data can be recovered from Alabama football history? University of Alabama Office of Research and Development Research Grants Committee Level 1 Program - $6,000. Funded</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=280011Main Page2025-12-05T17:23:46Z<p>Smaccall: /* Projects */</p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
<i>For current information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu]</i> <br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[https://ir.ua.edu/handle/123456789/17433 presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization.<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology.<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Consultant<br><br />
<i>formerly</i><br><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS, PhD<br></b><br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.liverpooluniversitypress.co.uk/doi/pdf/10.3828/indexer.2002.23.1.7 closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>Dr. Yu Gan</b> from Stevens Institute of Technology for digital image processing<br />
# <b>Dr. Greg Bott</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Publications_and_Presentations&diff=280010Publications and Presentations2025-11-30T22:17:55Z<p>Smaccall: </p>
<hr />
<div><b>Publications and Presentations of the [https://wikibase.slis.ua.edu/ Linked Data Research Group] at the University of Alabama SLIS.</b><br />
<br />
# Liu, Ho., Aderon, C., Wagon, N., Bamba, A. L., Li, X., Liu, Hu., MacCall, S. & Gan, Y. (2023). Automated player identification and indexing using two-stage deep learning network. Scientific Reports, 13, article number: 10036 (<b>available open access: [https://doi.org/10.1038/s41598-023-36657-5 https://doi.org/10.1038/s41598-023-36657-5]</b>).<br />
# Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/2022_asist_ai_workshop_presentation-liu_and_maccall-final.pdf presentation slides and speaker notes]</b>).<br />
# Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization.<br />
# Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology.<br />
# Nguyen Nguyen, Jeff Reidy, and Noah Wagnon (2021). "Final Presentation: Automated Jersey Number Recognition". Senior design team presentation for EECE 494.407 for 2020-21 academic year at The University of Alabama. <br />
# MacCall, S.L., & Liu, H. (2020). Data-driven semantic indexing of digital assets documenting American football game action. Poster presented at the 2020 annual meeting of the American Society for Information Science and Technology. [https://doi.org/10.1002/pra2.414 https://doi.org/10.1002/pra2.414]<br />
# MacCall, S.L., Bott, G., Anderson, C.M., & Liu, H. (2020). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase I: How much data can be recovered from Alabama football history? Presented at the University of Alabama 2020 Faculty Research Day, Tuscaloosa, AL. [https://tinyurl.com/yyr5exan Virtual session due to COVID19]<br />
# MacCall, S.L. (2020). Systems and methods for digital asset organization. U.S. Patent number 10,534,812. <br />
# MacCall, S.L., Liu, H., & Anderson, C.M. (2020). Statistical data recovery from historical documentation of Alabama football games using Wikibase as a repository. Interactive demonstration accepted for Connecting Collections as Data: Transforming Communities, Sharing Knowledge, and Building Networks with International GLAM Labs, Washington, DC. (Conference canceled due to COVID-19)<br />
# MacCall, S.L. (2020). Data-driven semantic DAM indexing incorporating statistical play-by-play game logs: A linked data application using Wikibase from the 2017 football season of the Alabama Crimson Tide. Conference paper presented at the 2020 LD4 Conference on Linked Data in Libraries, College Station, TX. ([https://tinyurl.com/y5wcxqen Video of presentation])<br />
# Anderson, C.M., Liu, H., & MacCall, S.L. (2020). Crowdsourcing in a semantic indexing workflow for efficiently organizing historical multimedia sports collections. Poster accepted for the 2020 Annual Meeting of the Alabama Library Association, Birmingham, AL. (Conference canceled due to COVID-19)<br />
# MacCall, S.L., Liu, H., & Anderson, C.M. (2019). How much statistical data can be recovered from Alabama football history? Piloting a crowdsourced approach using Wikibase as data repository. Conference paper presented at 2019 Digitorium Digital Humanities Conference, Tuscaloosa, AL. [UA Institutional Repository deposit: [https://ir.ua.edu/handle/123456789/6574 https://ir.ua.edu/handle/123456789/6574]]<br />
# MacCall, S.L. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 2: Developing SLIS research capacity for key linked open data technologies. University of Alabama School of Library and Information Studies Research Fund -$1,000. Funded. <br />
# MacCall, S.L., & Bott, G. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 1: How much data can be recovered from Alabama football history? University of Alabama Office of Research and Development Research Grants Committee Level 1 Program - $6,000. Funded</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Publications_and_Presentations&diff=280009Publications and Presentations2025-11-30T22:16:18Z<p>Smaccall: </p>
<hr />
<div><b>Publications and Presentations of the [https://wikibase.slis.ua.edu/ Linked Data Research Group] at the University of Alabama SLIS.</b><br />
<br />
# Liu, Ho., Aderon, C., Wagon, N., Bamba, A. L., Li, X., Liu, Hu., MacCall, S. & Gan, Y. (2023). Automated player identification and indexing using two-stage deep learning network. Scientific Reports, 13, article number: 10036 (<b>available open access: [https://doi.org/10.1038/s41598-023-36657-5 https://doi.org/10.1038/s41598-023-36657-5]</b>).<br />
# Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/2022_asist_ai_workshop_presentation-liu_and_maccall-final.pdf presentation slides and speaker notes]</b>).<br />
# Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization.<br />
# Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology.<br />
# Nguyen Nguyen, Jeff Reidy, and Noah Wagnon (2021). "Final Presentation: Automated Jersey Number Recognition". Senior design team presentation for EECE 494.407 for 2020-21 academic year at The University of Alabama. <br />
# MacCall, S.L., & Liu, H. (2020). Data-driven semantic indexing of digital assets documenting American football game action. Poster presented at the 2020 annual meeting of the American Society for Information Science and Technology. [https://doi.org/10.1002/pra2.414 https://doi.org/10.1002/pra2.414]<br />
# MacCall, S.L., Bott, G., Anderson, C.M., & Liu, H. (2020). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase I: How much data can be recovered from Alabama football history? Presented at the University of Alabama 2020 Faculty Research Day, Tuscaloosa, AL. [https://tinyurl.com/yyr5exan Virtual session due to COVID19]<br />
# MacCall, S.L. (2020). [http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/maccall-patent_us20160171028a1.pdf Systems and methods for digital asset organization]. U.S . Patent number 10,534,812. <br />
# MacCall, S.L., Liu, H., & Anderson, C.M. (2020). Statistical data recovery from historical documentation of Alabama football games using Wikibase as a repository. Interactive demonstration accepted for Connecting Collections as Data: Transforming Communities, Sharing Knowledge, and Building Networks with International GLAM Labs, Washington, DC. (Conference canceled due to COVID-19)<br />
# MacCall, S.L. (2020). Data-driven semantic DAM indexing incorporating statistical play-by-play game logs: A linked data application using Wikibase from the 2017 football season of the Alabama Crimson Tide. Conference paper presented at the 2020 LD4 Conference on Linked Data in Libraries, College Station, TX. ([https://tinyurl.com/y5wcxqen Video of presentation])<br />
# Anderson, C.M., Liu, H., & MacCall, S.L. (2020). Crowdsourcing in a semantic indexing workflow for efficiently organizing historical multimedia sports collections. Poster accepted for the 2020 Annual Meeting of the Alabama Library Association, Birmingham, AL. (Conference canceled due to COVID-19)<br />
# MacCall, S.L., Liu, H., & Anderson, C.M. (2019). How much statistical data can be recovered from Alabama football history? Piloting a crowdsourced approach using Wikibase as data repository. Conference paper presented at 2019 Digitorium Digital Humanities Conference, Tuscaloosa, AL. [UA Institutional Repository deposit: [https://ir.ua.edu/handle/123456789/6574 https://ir.ua.edu/handle/123456789/6574]]<br />
# MacCall, S.L. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 2: Developing SLIS research capacity for key linked open data technologies. University of Alabama School of Library and Information Studies Research Fund -$1,000. Funded. <br />
# MacCall, S.L., & Bott, G. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 1: How much data can be recovered from Alabama football history? University of Alabama Office of Research and Development Research Grants Committee Level 1 Program - $6,000. Funded</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=280008Main Page2025-11-30T22:10:13Z<p>Smaccall: /* Affiliate Researchers */</p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
<i>For current information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu]</i> <br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[https://ir.ua.edu/bitstreams/69a9a746-9003-4996-a5ec-d4597f1e762f/download presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization.<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology.<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Consultant<br><br />
<i>formerly</i><br><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS, PhD<br></b><br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.liverpooluniversitypress.co.uk/doi/pdf/10.3828/indexer.2002.23.1.7 closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>Dr. Yu Gan</b> from Stevens Institute of Technology for digital image processing<br />
# <b>Dr. Greg Bott</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=280007Main Page2025-11-30T22:07:46Z<p>Smaccall: /* Mission */</p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
<i>For current information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu]</i> <br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[https://ir.ua.edu/bitstreams/69a9a746-9003-4996-a5ec-d4597f1e762f/download presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization.<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology.<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Consultant<br><br />
<i>formerly</i><br><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS, PhD<br></b><br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.liverpooluniversitypress.co.uk/doi/pdf/10.3828/indexer.2002.23.1.7 closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=280006Main Page2025-11-30T22:04:40Z<p>Smaccall: /* Projects */</p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
<i>For current information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu]</i> <br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[https://ir.ua.edu/bitstreams/69a9a746-9003-4996-a5ec-d4597f1e762f/download presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization.<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology.<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Consultant<br><br />
<i>formerly</i><br><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS, PhD<br></b><br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Chronology:_Data-driven_Sports_Image_Indexing_Research&diff=280005Chronology: Data-driven Sports Image Indexing Research2025-11-30T21:59:41Z<p>Smaccall: /* 2021 */</p>
<hr />
<div><b>[https://wikibase.slis.ua.edu/ Linked Data Research Group]</b><br><br><br />
[https://smaccall.people.ua.edu Steven L. MacCall, PhD] and Huapu Liu<br><br />
[https://slis.ua.edu School of Library and Information Studies]<br><br />
[https://cis.ua.edu College of Communication and Information Sciences]<br><br />
University of Alabama<br><br />
<br />
<br />
==Introduction and Background==<br />
<br />
This chronology reports on research, teaching, service, and entrepreneurial activities investigating '''data-driven sports image indexing''' led by [https://smaccall.people.ua.edu Steven L. MacCall, PhD], Associate Professor in the School of Library and Information Studies (SLIS) at the University of Alabama.<br />
<br />
This research program investigates the effectiveness and efficiency of a semantic indexing method designed to facilitate image search queries that are pinpointed on game statistical situations (e.g., '''''retrieve all plays from the 2017 Alabama Crimson Tide football season that resulted in touchdowns and occurred in the 3rd quarter on 2nd down''''' - [https://tinyurl.com/vdjzq3x all such plays] / [https://tinyurl.com/tljdmt9 just those such plays having available '''video clips'''] => ''to run queries: press white arrow in blue box lower left''). The method, based on a [http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=10534812.PN.&OS=PN/10534812&RS=PN/10534812 recently issued UA patent], is not based on the traditional asset-by-asset approach that results in the creation of individual metadata records for each asset; rather, our method is innovative because it proposes to invert the traditional process by facilitating the organizing of each game on a play-by-play basis using structured time segments, and then we “attach” each asset to the appropriate play via an actual or simulated time-based parameter. This reduction in time spent organizing (fewer games versus indexing many more individual images) is the source of our claim for efficiency and effectiveness.<br />
<br />
The key to the success of this semantic indexing method was the design and development of a data processing pipeline that takes raw statistical play-by-play datasets and converts them to RDF triples so that they can be uploaded into our linked data application using a Wikibase instance hosted here at the University of Alabama.<br />
<br />
Indexing research is a long standing area for information science investigators. Applications of such research include the improvement of systems design (efficiency and effectiveness) as well as informing the teaching of information professionals to better prepare them for the technological intensities of the modern metadata work environment. In a professional school such as SLIS, it is vitally important that full time faculty members have teaching and service activities informed by their research.<br />
<br />
==Current Research Questions==<br />
<br />
One would expect that research questions would evolve over time, and in that spirit, these are the current primary research questions under investigation in this research program:<br />
<br />
# '''Effectiveness''': Can data-driven indexing methods serve a maximal indexing objective in which all identifiable named entities are indexed based on all of their observable features and attributes?<br />
# '''Efficiency''': Can data-driven indexing methods scale to meet the maximal indexing objective and also match the rate in which current sports images (photo and video) that are captured by digital cameras and historical sports images (photo and video) that are digitized from the historical record?<br />
# '''Integration''': Can current born digital sports images and digitized historical images be indexed in such a way that results in a single database application rather than in multiple silos?<br />
# '''Identification''': Can a maximal semantic indexing method incorporating statistical play-by-play datasets provide sufficient context for identifying images based on visible entities and attributes?<br />
<br />
==Data Sources (and Their Original Material Condition) Used in our Data Processing Pipeline==<br />
<br />
The data-driven method under study incorporates statistical play-by-play datasets on a per game basis into the image indexing process. We are currently focusing our investigation on Alabama Crimson Tide football based on publicly accessibility play-by-play datasets for 2017 and also based on our access to the historical data sources available at the Paul W. Bryant Museum at the University of Alabama.<br />
<br />
Data preparation involved the development of a data processing pipeline consisting of a series of steps including data reformatting, data cleaning, and the reconciling of entities with their respective properties in the ontology that we developed and implemented using our Wikibase instance.<br />
<br />
Our data sources varied based on their original material condition (JSON-formatted digital files or paper-based sources) and their completeness. As such, we identified three categories of datasets that fed into our data processing pipeline based on the following combination of conditions:<br />
# '''Category 1 games with JSON-formatted play-by-play datasets''': We obtained digital files containing JSON-formatted datasets for every game of the [https://wikibase.slis.ua.edu/wiki/2017_Alabama_Crimson_Tide_football_team 2017 Alabama Crimson Tide season] from [https://drive.google.com/drive/folders/0B13YvT1olZIfOFM5dDJ6TV9YNEk publicly accessible play-by-play datasets]. NOTE: These files are available for every NCAA football game since 2005, which figures into our future plans.<br />
# '''Category 2 games with paper-based typewritten play-by-play datasets''': We had volunteers transcribe the paper-based typewritten play-by-play datasets obtained from the documentary collection of the Paul W. Bryant Museum for the following two games of the [https://wikibase.slis.ua.edu/wiki/1992_Alabama_Crimson_Tide_football_team 1992 Alabama Crimson Tide season]:<br />
## [https://wikibase.slis.ua.edu/wiki/1992_Alabama_versus_Tulane_Football_Game Alabama versus Tulane] played on October 10, 1992 at the Louisiana Superdome in New Orleans<br />
## [https://wikibase.slis.ua.edu/wiki/1992_Alabama_versus_LSU_Football_Game Alabama versus LSU] played on November 7, 1992 at Tiger Stadium in Baton Rouge, LA<br />
# '''Category 3 games with no existing play-by-play datasets in any format''': Without access to play-by-play datasets as either digital files or in typewritten paper-based sources, we turned to newspaper accounts of games in order to attempt to reconstruct a play-by-play dataset for the following two games of the [https://wikibase.slis.ua.edu/wiki/1961_Alabama_Crimson_Tide_football_team 1961 Alabama Crimson Tide season]:<br />
## [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Tulane_Football_Game Alabama versus Tulane] played on September 30, 1961 at Ladd Stadium in Mobile, AL ('''Newspaper source''': "[https://news.google.com/newspapers?id=9mQ0AAAAIBAJ&sjid=JZoEAAAAIBAJ&pg=5616%2C9684 '''Alabama Nips Tulane 9 to 0''']" by Charles Land appearing in the October 1, 1961 edition of the Tuscaloosa News)<br />
## [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Vanderbilt_Football_Game Alabama versus Vanderbilt] played on October 7, 1961 at Legion Field in Birmingham ('''Newspaper source''': "[https://news.google.com/newspapers?id=_GQ0AAAAIBAJ&sjid=JZoEAAAAIBAJ&pg=7049%2C1068591 '''Tide Engulfs Vandy, 35-6''']" by Charles Land appearing in the October 8, 1961 edition of the Tuscaloosa News)<br />
<br />
During this investigation, we came across an additional game, the [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Auburn_Football_Game 1961 Iron Bowl] played on December 2, 1961 at Legion Field in Birmingham, that had already been fully reconstructed for the "Bama's Greatest Games" TV series so we added all of the plays from that game to our database. We would like to thank the Paul W. Bryant Museum for providing us with a digitized copy of the VHS version of this game.<br />
<br />
==Research Result Highlights and Links to Working Software Demonstrations==<br />
<br />
<b><i>See also [[Publications and Presentations]]</i></b><br />
<br />
===Semantic Indexing Era: Wikibase Software (2018 to present)===<br />
Since summer of 2018 (see entry for 2018 in chronology below), we have been able to take advantage of a state-of-the-art research and development software platform ([https://wikiba.se Wikibase]) that facilitates our investigation of research questions using semantic indexing methods rather than the using conventional image indexing software (see subsection just below).<br />
<br />
Since summer of 2018, we have achieved the following milestones related to our research questions (follow links for more information about each):<br />
<br />
# <u>Research questions 1 and 2: Image indexing efficiency and effectiveness</u><br />
## '''Proof of concept (spring 2022) - Successfully ingested play-by-play data for over 12,500 college football game from 2005 - 2021''': During the spring 2022 semester, we completed the design of an high-throughput ETL pipeline and successfully evaluated the pipeline by loading play-by-play statistical data for 17 college football seasons (2005-2021). <b>This load resulted in a [https://sportsdamkg.slis.ua.edu/sparql/ SPARQL queriable] graph database of over 2.5 million entities representing about 12,500 games and over 2.1 million plays that were run during those games.</b> This successful database load is a scaled up proof of concept for applying linked data technologies to organization of information challenges. <b>Sample SPARQL queries</b>:<br />
### [https://tinyurl.com/2k3j29ch All plays from the 2020 Alabama Crimson Tide football season with their corresponding game clock and wall clock time]<br><br />
### [https://tinyurl.com/yatkv4rt All plays from the 2018 Michigan Wolverine football season with their corresponding game clock and wall clock time]<br><br />
### [https://tinyurl.com/ybmq2gw9 All plays from the 2010 Alabama Crimson Tide football season with their corresponding game clock time]<br />
### [https://tinyurl.com/y9hs6wh9 All plays from the 2019 SEC football season (all teams) with their corresponding game clock and wall clock time]<br />
### [https://tinyurl.com/y7oyjllp Rushing touchdowns run for over 50 yards in 2021 SEC season games (all teams)]<br />
## '''Pilot study (spring 2019) - Successfully ingested play-by-play data for [https://wikibase.slis.ua.edu/wiki/2018-19_Academic_Year_Research_Report all 15 games from the 2017 Alabama Crimson Tide football season]''': We have completed the semantic indexing of every single play that occurred in all 15 games from the 2017 Alabama Crimson Tide football season using a semantic indexing method that incorporated JSON-formatted Web-accessible statistical play-by-play datasets into a semantic indexing process via the creation of a semi-automated data processing pipeline. This effort was partially funded by two 2018 grants that we received; see chronology below. '''''See [[2018-19 Academic Year Research Report]] for detailed documentation and software demonstration pertaining to the accomplishment this milestone.'''''<br />
## '''Pilot study of historical games (1) (spring 2020) - Successfully ingested partial play-by-play data from [https://wikibase.slis.ua.edu/wiki/2019-20_Academic_Year_Research_Progress_Report#Semantic_Indexing_of_2_Games_from_1992_Alabama_Football_Season 2 games from the 1992 Alabama Crimson Tide football season]''': We have completed RGC-grant funded work (see 2018 entry in chronology below) that allowed us to investigate the recovery of play-by-play data from the historical record. For games in 1992, the historical record consisted of typed play-by-play datasets requiring manual transcription by volunteers, which allowed us to investigate the inclusion into our data processing pipeline of a crowdsourcing method for historical data transcription. '''''See [[2019-20 Academic Year Research Progress Report]] for detailed documentation and software demonstration pertaining to the accomplishment this milestone.'''''<br />
## '''Pilot study of historical games (2) (spring 2020) - Successfully ingested partial play-by-play data from [https://wikibase.slis.ua.edu/wiki/2019-20_Academic_Year_Research_Progress_Report#Semantic_Indexing_of_2_Games_from_1961_Alabama_Football_Season 2 games from the 1961 Alabama Crimson Tide football season]''': As part of the above mentioned RGC grant, we also investigated games from the 1961 season. For games from 1961, there were no historical records available containing play-by-data. However, there are other documentary sources of such data, including journalistic newspaper accounts of those games. In our study, we investigated the inclusion into our data processing pipeline of a crowdsourcing method for historical data-extraction from newspaper articles. We chose to use articles published in the Tuscaloosa News available online. '''''See [[2019-20 Academic Year Research Progress Report]] for detailed documentation and software demonstration pertaining to the accomplishment this milestone.'''''<br />
## PLEASE NOTE: '''One additional game from the 1961 Alabama Crimson Tide football season''': When working with the 1961 season, we came across a fully reconstructed game, the "Iron Bowl" game against Auburn on December 2, 1961 that had been converted into a television production using digitized coaches film and historical accounts maintained at the Paul W. Bryant Museum. Based on the availability of this digitized full-game content, we were able to index every play of that game as if it were a game from the 2017 season (described above). '''''See [[2019-20 Academic Year Research Progress Report]] for detailed documentation and software demonstration pertaining to this game.'''''<br />
# <u>Research question 3: Integration</u>: After completing the semantic indexing of individual plays from the three seasons of Alabama Crimson Tide football as described above, we were able to successfully evaluate the querying of plays as if they were all in the same semantic database. Here are example SPARQL queries demonstrating this capability. ('''PLEASE NOTE: To run each query, click on the Blue Arrow icon in lower left portion of screen after clicking on links below'''):<br />
## [https://tinyurl.com/r2o3h23 All rushing touchdowns that went for over 50 yards] during the 3 seasons of Alabama Crimson Tide football in our database (just those for which there are [https://tinyurl.com/s5nzqgq video clips])<br />
## [https://tinyurl.com/v459wr2 All interceptions returned for 15 or more yards] during the 3 seasons of Alabama Crimson Tide football in our database (just those for which there are [https://tinyurl.com/u3zhzm2 video clips])<br />
## [https://tinyurl.com/s7td74m All successful field goals from 30 yards or more] during the 3 seasons of Alabama Crimson Tide football in our database (just those successful field goals for which there are [https://tinyurl.com/ts7qvlx video clips])<br />
# <u>[https://wikibase.slis.ua.edu/wiki/Plays_with_Example_UA_Images_May_2019 Research question 4: Identification]</u>: Crimson Tide Photos, a unit in UA Athletics, provided us with a random sample of born digital photos that were taken during games from the 2017 Alabama Crimson Tide football season, and we were able to identify each image in terms of the play context and the game statistical situation at the time that each photo was taken. ('''''See [[Plays with Example UA Images May 2019]] for documentation of this milestone.''''')<br />
<br />
===Conventional Indexing Era: Omeka and ContentDM Software (2008 to 2018)===<br />
During this period, SLIS students in my annual spring semester Metadata course (LS 566) benefited from the application of my research as I was able to use it to inform my teaching. Specifically, my research served as the basis for their course project work, which involved the application of indexing theories by way of applying the standard/conventional approach to the indexing of a set of images. These images were provided by Ken Gaddy, Director of the Paul W. Bryant Museum, and they were both digitized black and white photos of Alabama Crimson Tide football games from the 1975 season and also born digital color images from the 2010 National Championship game.<br />
<br />
The indexing of these images was accomplished using two different software applications. <br />
<br />
# [https://omeka.org/ Omeka software] (2011-2018): Here is a representative example of the end result of the last of such indexing projects from the [http://omeka.slis.ua.edu/ls566-spring2018/bama-football-images/items/browse Spring 2018 semester of this course].<br />
# [https://www.oclc.org/en/contentdm.html ContentDM] (2008-2010)<br />
<br />
==Research-related Accomplishments by Year==<br />
<br />
<b><i>See also [[Publications and Presentations]]</i></b><br />
<br />
The entries below are arranged reverse chronologically:<br />
<br />
===2023===<br />
Article published in a Nature Portfolio journal:<br />
<br />
Liu, Ho., Aderon, C., Wagon, N., Bamba, A. L., Li, X., Liu, Hu., MacCall, S. & Gan, Y. (2023). Automated player identification and indexing using two-stage deep learning network. Scientific Reports, 13, article number: 10036 (available open access: https://doi.org/10.1038/s41598-023-36657-5).<br />
<br />
===2022===<br />
During the spring 2022 semester, we completed the design of an high-throughput ETL pipeline and successfully evaluated the pipeline by loading play-by-play statistical data for 17 college football seasons (2005-2021). This load resulted in a [https://sportsdamkg.slis.ua.edu/sparql/ SPARQL queriable] graph database (using Wikibase software) of over 2.5 million entities representing about 12,500 games and over 2.1 million plays that were run during those games. This successful database load is a scaled up proof of concept for applying linked data technologies to organization of information challenges.<br><br />
<br>Sample SPARQL queries:<br><br />
* [https://tinyurl.com/yc25vjls All plays from the 2020 Alabama Crimson Tide football season with their corresponding game clock and wall clock time]<br><br />
* [https://tinyurl.com/yatkv4rt All plays from the 2018 Michigan Wolverine football season with their corresponding game clock and wall clock time]<br><br />
* [https://tinyurl.com/ybmq2gw9 All plays from the 2010 Alabama Crimson Tide football season with their corresponding game clock time]<br />
* [https://tinyurl.com/y9hs6wh9 All plays from the 2019 SEC football season (all teams) with their corresponding game clock and wall clock time]<br />
* [https://tinyurl.com/y7oyjllp Rushing touchdowns run for over 50 yards in 2021 SEC season games (all teams)]<br />
<br />
===2021===<br />
<br />
Design of ETL pipeline begins by Huapu Liu:<br />
* Python and R scripting for accessing and transforming game datasets from [https://collegefootballdata.com/ College Football Data's API]<br />
* Optimized data upload speed to our Wikibase instance via API<br />
<br />
Continuing work with Dr. Gan's group in the UA Department of Electrical and Computer Engineering. Completion of senior project work: Nguyen Nguyen, Jeff Reidy, and Noah Wagnon. "Final Presentation: Automated Jersey Number Recognition". Senior design team presentation for EECE 494.407 for 2020-21 academic year. [http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/494-407_final_pres_-_for_dr_gan.pdf PDF slidedeck]<br />
<br />
===2020===<br />
<br />
MacCall, S.L. (2020). Systems and methods for digital asset organization. U.S. Patent number 10,534,812. <br />
<br />
Partnership with Dr. Yu Gan, Assistant Professor of Electrical and Computer Engineering at the University of Alabama. Dr. Gan is an expert in digital image processing, and he is working with masters student Alexander Ramey to automatically detect player numbers in order to extract player participation data from born digital and digitized historical video: The 2017 season [https://wikibase.slis.ua.edu/wiki/2018_Alabama_versus_Georgia_CFP_National_Championship_Football_Game_Presented_by_AT%26T National Championship game] and the 1961 season [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Auburn_Football_Game Iron Bowl].<br />
<br />
MacCall, S.L., Liu, H., & Anderson, C.M. (2020). Statistical data recovery from historical documentation of Alabama football games using Wikibase as a repository. Interactive demonstration accepted for Connecting Collections as Data: Transforming Communities, Sharing Knowledge, and Building Networks with International GLAM Labs, Washington, DC.<br />
<br />
MacCall, S.L. (2020). Data-driven semantic DAM indexing incorporating statistical play-by-play game logs: A linked data application using Wikibase from the 2017 football season of the Alabama Crimson Tide. Conference paper accepted for presentation at the 2020 LD4 Conference on Linked Data in Libraries, College Station, TX.<br />
<br />
Anderson, C.M., Liu, H., & MacCall, S.L. (2020). Crowdsourcing in a semantic indexing workflow for efficiently organizing historical multimedia sports collections. Poster accepted for the 2020 Annual Meeting of the Alabama Library Association, Birmingham, AL. <br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Wikibase software, as the basis for the major indexing project in my brand new spring 2020 Linked Data course (LS 590)<br />
<br />
===2019===<br />
<br />
MacCall, S.L., Liu, H., & Anderson, C.M. (2019). How much statistical data can be recovered from Alabama football history? Piloting a crowdsourced approach using Wikibase as data repository. Conference paper presented at 2019 Digitorium Digital Humanities Conference, Tuscaloosa, AL. [UA Institutional Repository deposit: [https://ir.ua.edu/handle/123456789/6574 https://ir.ua.edu/handle/123456789/6574]]<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with OmekaS software, as the basis for the major metadata indexing project in my spring 2019 Metadata course (LS 566)<br />
<br />
===2018===<br />
<br />
MacCall, S.L. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 2: Developing SLIS research capacity for key linked open data technologies. University of Alabama School of Library and Information Studies Research Fund -$1,000. Funded. <br />
<br />
MacCall, S.L., & Bott, G. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 1: How much data can be recovered from Alabama football history? University of Alabama Office of Research and Development Research Grants Committee Level 1 Program - $6,000. Funded <br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with OmekaS software, as the basis for the major metadata indexing project in my spring 2018 Metadata course (LS 566)<br />
<br />
===2017===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with OmekaS software, as the basis for the major metadata indexing project in my spring 2017 Metadata course (LS 566)<br />
<br />
===2016===<br />
<br />
MacCall, S.L., McMillan, D.J., Vargo, C.J., Bradley, S.B., & Aversa, E.A. (2016). Efficiency, integration, interoperability: A 21st century approach to organizing sports digital assets for all libraries. Knight Foundation’s News Challenge for Libraries: How Might Libraries Serve 21st Century Information Needs? - pre-budget grant submission. Not funded.<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2016 Metadata course (LS 566)<br />
<br />
===2015===<br />
<br />
MacCall, S.L. (Filed December 15, 2015). Systems and methods for digital asset organization. U.S. Utility Patent Application number 14/971,463.<br />
<br />
MacCall, S.L., Vargo, C.J., Bradley, S.B., & Aversa, E.A. (2015). Development of a novel digital asset organizing method in sports. National Science Foundation - Small Business Innovation Research (SBIR) Phase I Grant - $225,000 ($74,925 sub-award to University of Alabama). Not funded. <br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2015 Metadata course (LS 566)<br />
<br />
===2014===<br />
<br />
MacCall, S.L. [Chief Scientist for MaxOrg, LLC], Aversa, E.A. [CEO for MaxOrg, LLC], & McMillan, D.J. [Technology Officer for MaxOrg, LLC]. (2014, 2015, 2016). Crimson Canvas - MaxOrg, LLC. Program participation: Commercial Development of Faculty Developed UA Intellectual Property sponsored by Alabama Innovation and Mentoring of Entrepreneurs (AIME).<br />
<br />
MaxOrg, LLC formed as a faculty-led startup to contribute to the commercial development of UA intellectual property (see 2020 and 2015 entries above for patent issued and patent filed data respectively)<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2014 Metadata course (LS 566)<br />
<br />
===2013===<br />
<br />
MacCall, S.L. & Gaddy, K. (2013). Optimal organizing of digital images in sports: A project of the Paul W. Bryant Museum and UA SLIS. Presented at the 2013 University of Alabama Program in Sports Communication Sports Symposium, Tuscaloosa, AL. [Slideshare: [http://tinyurl.com/k875g5j http://tinyurl.com/k875g5j]]<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2013 Metadata course (LS 566)<br />
<br />
===2011===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2011 Metadata course (LS 566)<br />
<br />
===2010===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with ContentDM software, as the basis for the major metadata indexing project in my spring 2010 Metadata course (LS 566)<br />
<br />
===2009===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with ContentDM software, as the basis for the major metadata indexing project in my spring 2009 Metadata course (LS 566)<br />
<br />
===2008===<br />
<br />
Partnership begun with Ken Gaddy, director of the Paul W. Bryant Museum. Ken provided my students and me with a set of digitized black and white photos from the 1975 Alabama Crimson Tide football season and a set of born digital color images from the 2010 National Championship game. These images served as the training set for students in my LS 566 course on Metadata every year since 2008. Equally importantly, my own study of these images was instrumental in the development of my research program and subsequently led to the research output reported here.<br />
<br />
Adopted use of ContentDM image repository software for the metadata indexing project in my LS 566 Metadata course at SLIS.<br />
<br />
==Team Members and Major Supporters==<br />
<br />
# Ken Gaddy, Director, Paul W. Bryant Museum (retired)<br />
# Dr. Greg Bott, Assistant Professor, UA Culverhouse College of Business (co-author of RGC grant)<br />
# David McMillan, Executive Director, Enterprise Development Application Support and MaxOrg, LLC (led install of Wikibase software and its software customization to this project)<br />
# Dr. Yu Gan, Assistant Professor, UA Department of Electrical and Computer Engineering (digital image processing)<br />
# Huapu Liu, MLIS, my former Graduate Research Assistant and frequent co-author (provided indispensable support from the very beginning of the Wikibase phase of this research project when we were faced with an empty database!)<br />
# C. Melissa Anderson, my current Graduate Research Assistant and co-author (project manager for RGC grant)<br />
# Christina Schultz-Richert, MLIS graduate (worked to extend the football related model to Public Television episode transcripts)<br />
# Dr. Elizabeth Aversa,retired SLIS Director and MaxOrg, LLC</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Chronology:_Data-driven_Sports_Image_Indexing_Research&diff=280004Chronology: Data-driven Sports Image Indexing Research2025-11-30T21:55:38Z<p>Smaccall: /* 2020 */</p>
<hr />
<div><b>[https://wikibase.slis.ua.edu/ Linked Data Research Group]</b><br><br><br />
[https://smaccall.people.ua.edu Steven L. MacCall, PhD] and Huapu Liu<br><br />
[https://slis.ua.edu School of Library and Information Studies]<br><br />
[https://cis.ua.edu College of Communication and Information Sciences]<br><br />
University of Alabama<br><br />
<br />
<br />
==Introduction and Background==<br />
<br />
This chronology reports on research, teaching, service, and entrepreneurial activities investigating '''data-driven sports image indexing''' led by [https://smaccall.people.ua.edu Steven L. MacCall, PhD], Associate Professor in the School of Library and Information Studies (SLIS) at the University of Alabama.<br />
<br />
This research program investigates the effectiveness and efficiency of a semantic indexing method designed to facilitate image search queries that are pinpointed on game statistical situations (e.g., '''''retrieve all plays from the 2017 Alabama Crimson Tide football season that resulted in touchdowns and occurred in the 3rd quarter on 2nd down''''' - [https://tinyurl.com/vdjzq3x all such plays] / [https://tinyurl.com/tljdmt9 just those such plays having available '''video clips'''] => ''to run queries: press white arrow in blue box lower left''). The method, based on a [http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=10534812.PN.&OS=PN/10534812&RS=PN/10534812 recently issued UA patent], is not based on the traditional asset-by-asset approach that results in the creation of individual metadata records for each asset; rather, our method is innovative because it proposes to invert the traditional process by facilitating the organizing of each game on a play-by-play basis using structured time segments, and then we “attach” each asset to the appropriate play via an actual or simulated time-based parameter. This reduction in time spent organizing (fewer games versus indexing many more individual images) is the source of our claim for efficiency and effectiveness.<br />
<br />
The key to the success of this semantic indexing method was the design and development of a data processing pipeline that takes raw statistical play-by-play datasets and converts them to RDF triples so that they can be uploaded into our linked data application using a Wikibase instance hosted here at the University of Alabama.<br />
<br />
Indexing research is a long standing area for information science investigators. Applications of such research include the improvement of systems design (efficiency and effectiveness) as well as informing the teaching of information professionals to better prepare them for the technological intensities of the modern metadata work environment. In a professional school such as SLIS, it is vitally important that full time faculty members have teaching and service activities informed by their research.<br />
<br />
==Current Research Questions==<br />
<br />
One would expect that research questions would evolve over time, and in that spirit, these are the current primary research questions under investigation in this research program:<br />
<br />
# '''Effectiveness''': Can data-driven indexing methods serve a maximal indexing objective in which all identifiable named entities are indexed based on all of their observable features and attributes?<br />
# '''Efficiency''': Can data-driven indexing methods scale to meet the maximal indexing objective and also match the rate in which current sports images (photo and video) that are captured by digital cameras and historical sports images (photo and video) that are digitized from the historical record?<br />
# '''Integration''': Can current born digital sports images and digitized historical images be indexed in such a way that results in a single database application rather than in multiple silos?<br />
# '''Identification''': Can a maximal semantic indexing method incorporating statistical play-by-play datasets provide sufficient context for identifying images based on visible entities and attributes?<br />
<br />
==Data Sources (and Their Original Material Condition) Used in our Data Processing Pipeline==<br />
<br />
The data-driven method under study incorporates statistical play-by-play datasets on a per game basis into the image indexing process. We are currently focusing our investigation on Alabama Crimson Tide football based on publicly accessibility play-by-play datasets for 2017 and also based on our access to the historical data sources available at the Paul W. Bryant Museum at the University of Alabama.<br />
<br />
Data preparation involved the development of a data processing pipeline consisting of a series of steps including data reformatting, data cleaning, and the reconciling of entities with their respective properties in the ontology that we developed and implemented using our Wikibase instance.<br />
<br />
Our data sources varied based on their original material condition (JSON-formatted digital files or paper-based sources) and their completeness. As such, we identified three categories of datasets that fed into our data processing pipeline based on the following combination of conditions:<br />
# '''Category 1 games with JSON-formatted play-by-play datasets''': We obtained digital files containing JSON-formatted datasets for every game of the [https://wikibase.slis.ua.edu/wiki/2017_Alabama_Crimson_Tide_football_team 2017 Alabama Crimson Tide season] from [https://drive.google.com/drive/folders/0B13YvT1olZIfOFM5dDJ6TV9YNEk publicly accessible play-by-play datasets]. NOTE: These files are available for every NCAA football game since 2005, which figures into our future plans.<br />
# '''Category 2 games with paper-based typewritten play-by-play datasets''': We had volunteers transcribe the paper-based typewritten play-by-play datasets obtained from the documentary collection of the Paul W. Bryant Museum for the following two games of the [https://wikibase.slis.ua.edu/wiki/1992_Alabama_Crimson_Tide_football_team 1992 Alabama Crimson Tide season]:<br />
## [https://wikibase.slis.ua.edu/wiki/1992_Alabama_versus_Tulane_Football_Game Alabama versus Tulane] played on October 10, 1992 at the Louisiana Superdome in New Orleans<br />
## [https://wikibase.slis.ua.edu/wiki/1992_Alabama_versus_LSU_Football_Game Alabama versus LSU] played on November 7, 1992 at Tiger Stadium in Baton Rouge, LA<br />
# '''Category 3 games with no existing play-by-play datasets in any format''': Without access to play-by-play datasets as either digital files or in typewritten paper-based sources, we turned to newspaper accounts of games in order to attempt to reconstruct a play-by-play dataset for the following two games of the [https://wikibase.slis.ua.edu/wiki/1961_Alabama_Crimson_Tide_football_team 1961 Alabama Crimson Tide season]:<br />
## [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Tulane_Football_Game Alabama versus Tulane] played on September 30, 1961 at Ladd Stadium in Mobile, AL ('''Newspaper source''': "[https://news.google.com/newspapers?id=9mQ0AAAAIBAJ&sjid=JZoEAAAAIBAJ&pg=5616%2C9684 '''Alabama Nips Tulane 9 to 0''']" by Charles Land appearing in the October 1, 1961 edition of the Tuscaloosa News)<br />
## [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Vanderbilt_Football_Game Alabama versus Vanderbilt] played on October 7, 1961 at Legion Field in Birmingham ('''Newspaper source''': "[https://news.google.com/newspapers?id=_GQ0AAAAIBAJ&sjid=JZoEAAAAIBAJ&pg=7049%2C1068591 '''Tide Engulfs Vandy, 35-6''']" by Charles Land appearing in the October 8, 1961 edition of the Tuscaloosa News)<br />
<br />
During this investigation, we came across an additional game, the [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Auburn_Football_Game 1961 Iron Bowl] played on December 2, 1961 at Legion Field in Birmingham, that had already been fully reconstructed for the "Bama's Greatest Games" TV series so we added all of the plays from that game to our database. We would like to thank the Paul W. Bryant Museum for providing us with a digitized copy of the VHS version of this game.<br />
<br />
==Research Result Highlights and Links to Working Software Demonstrations==<br />
<br />
<b><i>See also [[Publications and Presentations]]</i></b><br />
<br />
===Semantic Indexing Era: Wikibase Software (2018 to present)===<br />
Since summer of 2018 (see entry for 2018 in chronology below), we have been able to take advantage of a state-of-the-art research and development software platform ([https://wikiba.se Wikibase]) that facilitates our investigation of research questions using semantic indexing methods rather than the using conventional image indexing software (see subsection just below).<br />
<br />
Since summer of 2018, we have achieved the following milestones related to our research questions (follow links for more information about each):<br />
<br />
# <u>Research questions 1 and 2: Image indexing efficiency and effectiveness</u><br />
## '''Proof of concept (spring 2022) - Successfully ingested play-by-play data for over 12,500 college football game from 2005 - 2021''': During the spring 2022 semester, we completed the design of an high-throughput ETL pipeline and successfully evaluated the pipeline by loading play-by-play statistical data for 17 college football seasons (2005-2021). <b>This load resulted in a [https://sportsdamkg.slis.ua.edu/sparql/ SPARQL queriable] graph database of over 2.5 million entities representing about 12,500 games and over 2.1 million plays that were run during those games.</b> This successful database load is a scaled up proof of concept for applying linked data technologies to organization of information challenges. <b>Sample SPARQL queries</b>:<br />
### [https://tinyurl.com/2k3j29ch All plays from the 2020 Alabama Crimson Tide football season with their corresponding game clock and wall clock time]<br><br />
### [https://tinyurl.com/yatkv4rt All plays from the 2018 Michigan Wolverine football season with their corresponding game clock and wall clock time]<br><br />
### [https://tinyurl.com/ybmq2gw9 All plays from the 2010 Alabama Crimson Tide football season with their corresponding game clock time]<br />
### [https://tinyurl.com/y9hs6wh9 All plays from the 2019 SEC football season (all teams) with their corresponding game clock and wall clock time]<br />
### [https://tinyurl.com/y7oyjllp Rushing touchdowns run for over 50 yards in 2021 SEC season games (all teams)]<br />
## '''Pilot study (spring 2019) - Successfully ingested play-by-play data for [https://wikibase.slis.ua.edu/wiki/2018-19_Academic_Year_Research_Report all 15 games from the 2017 Alabama Crimson Tide football season]''': We have completed the semantic indexing of every single play that occurred in all 15 games from the 2017 Alabama Crimson Tide football season using a semantic indexing method that incorporated JSON-formatted Web-accessible statistical play-by-play datasets into a semantic indexing process via the creation of a semi-automated data processing pipeline. This effort was partially funded by two 2018 grants that we received; see chronology below. '''''See [[2018-19 Academic Year Research Report]] for detailed documentation and software demonstration pertaining to the accomplishment this milestone.'''''<br />
## '''Pilot study of historical games (1) (spring 2020) - Successfully ingested partial play-by-play data from [https://wikibase.slis.ua.edu/wiki/2019-20_Academic_Year_Research_Progress_Report#Semantic_Indexing_of_2_Games_from_1992_Alabama_Football_Season 2 games from the 1992 Alabama Crimson Tide football season]''': We have completed RGC-grant funded work (see 2018 entry in chronology below) that allowed us to investigate the recovery of play-by-play data from the historical record. For games in 1992, the historical record consisted of typed play-by-play datasets requiring manual transcription by volunteers, which allowed us to investigate the inclusion into our data processing pipeline of a crowdsourcing method for historical data transcription. '''''See [[2019-20 Academic Year Research Progress Report]] for detailed documentation and software demonstration pertaining to the accomplishment this milestone.'''''<br />
## '''Pilot study of historical games (2) (spring 2020) - Successfully ingested partial play-by-play data from [https://wikibase.slis.ua.edu/wiki/2019-20_Academic_Year_Research_Progress_Report#Semantic_Indexing_of_2_Games_from_1961_Alabama_Football_Season 2 games from the 1961 Alabama Crimson Tide football season]''': As part of the above mentioned RGC grant, we also investigated games from the 1961 season. For games from 1961, there were no historical records available containing play-by-data. However, there are other documentary sources of such data, including journalistic newspaper accounts of those games. In our study, we investigated the inclusion into our data processing pipeline of a crowdsourcing method for historical data-extraction from newspaper articles. We chose to use articles published in the Tuscaloosa News available online. '''''See [[2019-20 Academic Year Research Progress Report]] for detailed documentation and software demonstration pertaining to the accomplishment this milestone.'''''<br />
## PLEASE NOTE: '''One additional game from the 1961 Alabama Crimson Tide football season''': When working with the 1961 season, we came across a fully reconstructed game, the "Iron Bowl" game against Auburn on December 2, 1961 that had been converted into a television production using digitized coaches film and historical accounts maintained at the Paul W. Bryant Museum. Based on the availability of this digitized full-game content, we were able to index every play of that game as if it were a game from the 2017 season (described above). '''''See [[2019-20 Academic Year Research Progress Report]] for detailed documentation and software demonstration pertaining to this game.'''''<br />
# <u>Research question 3: Integration</u>: After completing the semantic indexing of individual plays from the three seasons of Alabama Crimson Tide football as described above, we were able to successfully evaluate the querying of plays as if they were all in the same semantic database. Here are example SPARQL queries demonstrating this capability. ('''PLEASE NOTE: To run each query, click on the Blue Arrow icon in lower left portion of screen after clicking on links below'''):<br />
## [https://tinyurl.com/r2o3h23 All rushing touchdowns that went for over 50 yards] during the 3 seasons of Alabama Crimson Tide football in our database (just those for which there are [https://tinyurl.com/s5nzqgq video clips])<br />
## [https://tinyurl.com/v459wr2 All interceptions returned for 15 or more yards] during the 3 seasons of Alabama Crimson Tide football in our database (just those for which there are [https://tinyurl.com/u3zhzm2 video clips])<br />
## [https://tinyurl.com/s7td74m All successful field goals from 30 yards or more] during the 3 seasons of Alabama Crimson Tide football in our database (just those successful field goals for which there are [https://tinyurl.com/ts7qvlx video clips])<br />
# <u>[https://wikibase.slis.ua.edu/wiki/Plays_with_Example_UA_Images_May_2019 Research question 4: Identification]</u>: Crimson Tide Photos, a unit in UA Athletics, provided us with a random sample of born digital photos that were taken during games from the 2017 Alabama Crimson Tide football season, and we were able to identify each image in terms of the play context and the game statistical situation at the time that each photo was taken. ('''''See [[Plays with Example UA Images May 2019]] for documentation of this milestone.''''')<br />
<br />
===Conventional Indexing Era: Omeka and ContentDM Software (2008 to 2018)===<br />
During this period, SLIS students in my annual spring semester Metadata course (LS 566) benefited from the application of my research as I was able to use it to inform my teaching. Specifically, my research served as the basis for their course project work, which involved the application of indexing theories by way of applying the standard/conventional approach to the indexing of a set of images. These images were provided by Ken Gaddy, Director of the Paul W. Bryant Museum, and they were both digitized black and white photos of Alabama Crimson Tide football games from the 1975 season and also born digital color images from the 2010 National Championship game.<br />
<br />
The indexing of these images was accomplished using two different software applications. <br />
<br />
# [https://omeka.org/ Omeka software] (2011-2018): Here is a representative example of the end result of the last of such indexing projects from the [http://omeka.slis.ua.edu/ls566-spring2018/bama-football-images/items/browse Spring 2018 semester of this course].<br />
# [https://www.oclc.org/en/contentdm.html ContentDM] (2008-2010)<br />
<br />
==Research-related Accomplishments by Year==<br />
<br />
<b><i>See also [[Publications and Presentations]]</i></b><br />
<br />
The entries below are arranged reverse chronologically:<br />
<br />
===2023===<br />
Article published in a Nature Portfolio journal:<br />
<br />
Liu, Ho., Aderon, C., Wagon, N., Bamba, A. L., Li, X., Liu, Hu., MacCall, S. & Gan, Y. (2023). Automated player identification and indexing using two-stage deep learning network. Scientific Reports, 13, article number: 10036 (available open access: https://doi.org/10.1038/s41598-023-36657-5).<br />
<br />
===2022===<br />
During the spring 2022 semester, we completed the design of an high-throughput ETL pipeline and successfully evaluated the pipeline by loading play-by-play statistical data for 17 college football seasons (2005-2021). This load resulted in a [https://sportsdamkg.slis.ua.edu/sparql/ SPARQL queriable] graph database (using Wikibase software) of over 2.5 million entities representing about 12,500 games and over 2.1 million plays that were run during those games. This successful database load is a scaled up proof of concept for applying linked data technologies to organization of information challenges.<br><br />
<br>Sample SPARQL queries:<br><br />
* [https://tinyurl.com/yc25vjls All plays from the 2020 Alabama Crimson Tide football season with their corresponding game clock and wall clock time]<br><br />
* [https://tinyurl.com/yatkv4rt All plays from the 2018 Michigan Wolverine football season with their corresponding game clock and wall clock time]<br><br />
* [https://tinyurl.com/ybmq2gw9 All plays from the 2010 Alabama Crimson Tide football season with their corresponding game clock time]<br />
* [https://tinyurl.com/y9hs6wh9 All plays from the 2019 SEC football season (all teams) with their corresponding game clock and wall clock time]<br />
* [https://tinyurl.com/y7oyjllp Rushing touchdowns run for over 50 yards in 2021 SEC season games (all teams)]<br />
<br />
===2021===<br />
<br />
Design of ETL pipeline begins by Huapu Liu:<br />
* Python and R scripting for accessing and transforming game datasets from [https://api.collegefootballdata.com/api/docs/?url=/api-docs.json College Football Data's API]<br />
* Optimized data upload speed to our Wikibase instance via API<br />
<br />
Continuing work with Dr. Gan's group in the UA Department of Electrical and Computer Engineering. Completion of senior project work: Nguyen Nguyen, Jeff Reidy, and Noah Wagnon. "Final Presentation: Automated Jersey Number Recognition". Senior design team presentation for EECE 494.407 for 2020-21 academic year. [http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/494-407_final_pres_-_for_dr_gan.pdf PDF slidedeck]<br />
<br />
===2020===<br />
<br />
MacCall, S.L. (2020). Systems and methods for digital asset organization. U.S. Patent number 10,534,812. <br />
<br />
Partnership with Dr. Yu Gan, Assistant Professor of Electrical and Computer Engineering at the University of Alabama. Dr. Gan is an expert in digital image processing, and he is working with masters student Alexander Ramey to automatically detect player numbers in order to extract player participation data from born digital and digitized historical video: The 2017 season [https://wikibase.slis.ua.edu/wiki/2018_Alabama_versus_Georgia_CFP_National_Championship_Football_Game_Presented_by_AT%26T National Championship game] and the 1961 season [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Auburn_Football_Game Iron Bowl].<br />
<br />
MacCall, S.L., Liu, H., & Anderson, C.M. (2020). Statistical data recovery from historical documentation of Alabama football games using Wikibase as a repository. Interactive demonstration accepted for Connecting Collections as Data: Transforming Communities, Sharing Knowledge, and Building Networks with International GLAM Labs, Washington, DC.<br />
<br />
MacCall, S.L. (2020). Data-driven semantic DAM indexing incorporating statistical play-by-play game logs: A linked data application using Wikibase from the 2017 football season of the Alabama Crimson Tide. Conference paper accepted for presentation at the 2020 LD4 Conference on Linked Data in Libraries, College Station, TX.<br />
<br />
Anderson, C.M., Liu, H., & MacCall, S.L. (2020). Crowdsourcing in a semantic indexing workflow for efficiently organizing historical multimedia sports collections. Poster accepted for the 2020 Annual Meeting of the Alabama Library Association, Birmingham, AL. <br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Wikibase software, as the basis for the major indexing project in my brand new spring 2020 Linked Data course (LS 590)<br />
<br />
===2019===<br />
<br />
MacCall, S.L., Liu, H., & Anderson, C.M. (2019). How much statistical data can be recovered from Alabama football history? Piloting a crowdsourced approach using Wikibase as data repository. Conference paper presented at 2019 Digitorium Digital Humanities Conference, Tuscaloosa, AL. [UA Institutional Repository deposit: [https://ir.ua.edu/handle/123456789/6574 https://ir.ua.edu/handle/123456789/6574]]<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with OmekaS software, as the basis for the major metadata indexing project in my spring 2019 Metadata course (LS 566)<br />
<br />
===2018===<br />
<br />
MacCall, S.L. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 2: Developing SLIS research capacity for key linked open data technologies. University of Alabama School of Library and Information Studies Research Fund -$1,000. Funded. <br />
<br />
MacCall, S.L., & Bott, G. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 1: How much data can be recovered from Alabama football history? University of Alabama Office of Research and Development Research Grants Committee Level 1 Program - $6,000. Funded <br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with OmekaS software, as the basis for the major metadata indexing project in my spring 2018 Metadata course (LS 566)<br />
<br />
===2017===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with OmekaS software, as the basis for the major metadata indexing project in my spring 2017 Metadata course (LS 566)<br />
<br />
===2016===<br />
<br />
MacCall, S.L., McMillan, D.J., Vargo, C.J., Bradley, S.B., & Aversa, E.A. (2016). Efficiency, integration, interoperability: A 21st century approach to organizing sports digital assets for all libraries. Knight Foundation’s News Challenge for Libraries: How Might Libraries Serve 21st Century Information Needs? - pre-budget grant submission. Not funded.<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2016 Metadata course (LS 566)<br />
<br />
===2015===<br />
<br />
MacCall, S.L. (Filed December 15, 2015). Systems and methods for digital asset organization. U.S. Utility Patent Application number 14/971,463.<br />
<br />
MacCall, S.L., Vargo, C.J., Bradley, S.B., & Aversa, E.A. (2015). Development of a novel digital asset organizing method in sports. National Science Foundation - Small Business Innovation Research (SBIR) Phase I Grant - $225,000 ($74,925 sub-award to University of Alabama). Not funded. <br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2015 Metadata course (LS 566)<br />
<br />
===2014===<br />
<br />
MacCall, S.L. [Chief Scientist for MaxOrg, LLC], Aversa, E.A. [CEO for MaxOrg, LLC], & McMillan, D.J. [Technology Officer for MaxOrg, LLC]. (2014, 2015, 2016). Crimson Canvas - MaxOrg, LLC. Program participation: Commercial Development of Faculty Developed UA Intellectual Property sponsored by Alabama Innovation and Mentoring of Entrepreneurs (AIME).<br />
<br />
MaxOrg, LLC formed as a faculty-led startup to contribute to the commercial development of UA intellectual property (see 2020 and 2015 entries above for patent issued and patent filed data respectively)<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2014 Metadata course (LS 566)<br />
<br />
===2013===<br />
<br />
MacCall, S.L. & Gaddy, K. (2013). Optimal organizing of digital images in sports: A project of the Paul W. Bryant Museum and UA SLIS. Presented at the 2013 University of Alabama Program in Sports Communication Sports Symposium, Tuscaloosa, AL. [Slideshare: [http://tinyurl.com/k875g5j http://tinyurl.com/k875g5j]]<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2013 Metadata course (LS 566)<br />
<br />
===2011===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2011 Metadata course (LS 566)<br />
<br />
===2010===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with ContentDM software, as the basis for the major metadata indexing project in my spring 2010 Metadata course (LS 566)<br />
<br />
===2009===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with ContentDM software, as the basis for the major metadata indexing project in my spring 2009 Metadata course (LS 566)<br />
<br />
===2008===<br />
<br />
Partnership begun with Ken Gaddy, director of the Paul W. Bryant Museum. Ken provided my students and me with a set of digitized black and white photos from the 1975 Alabama Crimson Tide football season and a set of born digital color images from the 2010 National Championship game. These images served as the training set for students in my LS 566 course on Metadata every year since 2008. Equally importantly, my own study of these images was instrumental in the development of my research program and subsequently led to the research output reported here.<br />
<br />
Adopted use of ContentDM image repository software for the metadata indexing project in my LS 566 Metadata course at SLIS.<br />
<br />
==Team Members and Major Supporters==<br />
<br />
# Ken Gaddy, Director, Paul W. Bryant Museum (retired)<br />
# Dr. Greg Bott, Assistant Professor, UA Culverhouse College of Business (co-author of RGC grant)<br />
# David McMillan, Executive Director, Enterprise Development Application Support and MaxOrg, LLC (led install of Wikibase software and its software customization to this project)<br />
# Dr. Yu Gan, Assistant Professor, UA Department of Electrical and Computer Engineering (digital image processing)<br />
# Huapu Liu, MLIS, my former Graduate Research Assistant and frequent co-author (provided indispensable support from the very beginning of the Wikibase phase of this research project when we were faced with an empty database!)<br />
# C. Melissa Anderson, my current Graduate Research Assistant and co-author (project manager for RGC grant)<br />
# Christina Schultz-Richert, MLIS graduate (worked to extend the football related model to Public Television episode transcripts)<br />
# Dr. Elizabeth Aversa,retired SLIS Director and MaxOrg, LLC</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Chronology:_Data-driven_Sports_Image_Indexing_Research&diff=280003Chronology: Data-driven Sports Image Indexing Research2025-11-30T21:55:06Z<p>Smaccall: /* 2020 */</p>
<hr />
<div><b>[https://wikibase.slis.ua.edu/ Linked Data Research Group]</b><br><br><br />
[https://smaccall.people.ua.edu Steven L. MacCall, PhD] and Huapu Liu<br><br />
[https://slis.ua.edu School of Library and Information Studies]<br><br />
[https://cis.ua.edu College of Communication and Information Sciences]<br><br />
University of Alabama<br><br />
<br />
<br />
==Introduction and Background==<br />
<br />
This chronology reports on research, teaching, service, and entrepreneurial activities investigating '''data-driven sports image indexing''' led by [https://smaccall.people.ua.edu Steven L. MacCall, PhD], Associate Professor in the School of Library and Information Studies (SLIS) at the University of Alabama.<br />
<br />
This research program investigates the effectiveness and efficiency of a semantic indexing method designed to facilitate image search queries that are pinpointed on game statistical situations (e.g., '''''retrieve all plays from the 2017 Alabama Crimson Tide football season that resulted in touchdowns and occurred in the 3rd quarter on 2nd down''''' - [https://tinyurl.com/vdjzq3x all such plays] / [https://tinyurl.com/tljdmt9 just those such plays having available '''video clips'''] => ''to run queries: press white arrow in blue box lower left''). The method, based on a [http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=10534812.PN.&OS=PN/10534812&RS=PN/10534812 recently issued UA patent], is not based on the traditional asset-by-asset approach that results in the creation of individual metadata records for each asset; rather, our method is innovative because it proposes to invert the traditional process by facilitating the organizing of each game on a play-by-play basis using structured time segments, and then we “attach” each asset to the appropriate play via an actual or simulated time-based parameter. This reduction in time spent organizing (fewer games versus indexing many more individual images) is the source of our claim for efficiency and effectiveness.<br />
<br />
The key to the success of this semantic indexing method was the design and development of a data processing pipeline that takes raw statistical play-by-play datasets and converts them to RDF triples so that they can be uploaded into our linked data application using a Wikibase instance hosted here at the University of Alabama.<br />
<br />
Indexing research is a long standing area for information science investigators. Applications of such research include the improvement of systems design (efficiency and effectiveness) as well as informing the teaching of information professionals to better prepare them for the technological intensities of the modern metadata work environment. In a professional school such as SLIS, it is vitally important that full time faculty members have teaching and service activities informed by their research.<br />
<br />
==Current Research Questions==<br />
<br />
One would expect that research questions would evolve over time, and in that spirit, these are the current primary research questions under investigation in this research program:<br />
<br />
# '''Effectiveness''': Can data-driven indexing methods serve a maximal indexing objective in which all identifiable named entities are indexed based on all of their observable features and attributes?<br />
# '''Efficiency''': Can data-driven indexing methods scale to meet the maximal indexing objective and also match the rate in which current sports images (photo and video) that are captured by digital cameras and historical sports images (photo and video) that are digitized from the historical record?<br />
# '''Integration''': Can current born digital sports images and digitized historical images be indexed in such a way that results in a single database application rather than in multiple silos?<br />
# '''Identification''': Can a maximal semantic indexing method incorporating statistical play-by-play datasets provide sufficient context for identifying images based on visible entities and attributes?<br />
<br />
==Data Sources (and Their Original Material Condition) Used in our Data Processing Pipeline==<br />
<br />
The data-driven method under study incorporates statistical play-by-play datasets on a per game basis into the image indexing process. We are currently focusing our investigation on Alabama Crimson Tide football based on publicly accessibility play-by-play datasets for 2017 and also based on our access to the historical data sources available at the Paul W. Bryant Museum at the University of Alabama.<br />
<br />
Data preparation involved the development of a data processing pipeline consisting of a series of steps including data reformatting, data cleaning, and the reconciling of entities with their respective properties in the ontology that we developed and implemented using our Wikibase instance.<br />
<br />
Our data sources varied based on their original material condition (JSON-formatted digital files or paper-based sources) and their completeness. As such, we identified three categories of datasets that fed into our data processing pipeline based on the following combination of conditions:<br />
# '''Category 1 games with JSON-formatted play-by-play datasets''': We obtained digital files containing JSON-formatted datasets for every game of the [https://wikibase.slis.ua.edu/wiki/2017_Alabama_Crimson_Tide_football_team 2017 Alabama Crimson Tide season] from [https://drive.google.com/drive/folders/0B13YvT1olZIfOFM5dDJ6TV9YNEk publicly accessible play-by-play datasets]. NOTE: These files are available for every NCAA football game since 2005, which figures into our future plans.<br />
# '''Category 2 games with paper-based typewritten play-by-play datasets''': We had volunteers transcribe the paper-based typewritten play-by-play datasets obtained from the documentary collection of the Paul W. Bryant Museum for the following two games of the [https://wikibase.slis.ua.edu/wiki/1992_Alabama_Crimson_Tide_football_team 1992 Alabama Crimson Tide season]:<br />
## [https://wikibase.slis.ua.edu/wiki/1992_Alabama_versus_Tulane_Football_Game Alabama versus Tulane] played on October 10, 1992 at the Louisiana Superdome in New Orleans<br />
## [https://wikibase.slis.ua.edu/wiki/1992_Alabama_versus_LSU_Football_Game Alabama versus LSU] played on November 7, 1992 at Tiger Stadium in Baton Rouge, LA<br />
# '''Category 3 games with no existing play-by-play datasets in any format''': Without access to play-by-play datasets as either digital files or in typewritten paper-based sources, we turned to newspaper accounts of games in order to attempt to reconstruct a play-by-play dataset for the following two games of the [https://wikibase.slis.ua.edu/wiki/1961_Alabama_Crimson_Tide_football_team 1961 Alabama Crimson Tide season]:<br />
## [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Tulane_Football_Game Alabama versus Tulane] played on September 30, 1961 at Ladd Stadium in Mobile, AL ('''Newspaper source''': "[https://news.google.com/newspapers?id=9mQ0AAAAIBAJ&sjid=JZoEAAAAIBAJ&pg=5616%2C9684 '''Alabama Nips Tulane 9 to 0''']" by Charles Land appearing in the October 1, 1961 edition of the Tuscaloosa News)<br />
## [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Vanderbilt_Football_Game Alabama versus Vanderbilt] played on October 7, 1961 at Legion Field in Birmingham ('''Newspaper source''': "[https://news.google.com/newspapers?id=_GQ0AAAAIBAJ&sjid=JZoEAAAAIBAJ&pg=7049%2C1068591 '''Tide Engulfs Vandy, 35-6''']" by Charles Land appearing in the October 8, 1961 edition of the Tuscaloosa News)<br />
<br />
During this investigation, we came across an additional game, the [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Auburn_Football_Game 1961 Iron Bowl] played on December 2, 1961 at Legion Field in Birmingham, that had already been fully reconstructed for the "Bama's Greatest Games" TV series so we added all of the plays from that game to our database. We would like to thank the Paul W. Bryant Museum for providing us with a digitized copy of the VHS version of this game.<br />
<br />
==Research Result Highlights and Links to Working Software Demonstrations==<br />
<br />
<b><i>See also [[Publications and Presentations]]</i></b><br />
<br />
===Semantic Indexing Era: Wikibase Software (2018 to present)===<br />
Since summer of 2018 (see entry for 2018 in chronology below), we have been able to take advantage of a state-of-the-art research and development software platform ([https://wikiba.se Wikibase]) that facilitates our investigation of research questions using semantic indexing methods rather than the using conventional image indexing software (see subsection just below).<br />
<br />
Since summer of 2018, we have achieved the following milestones related to our research questions (follow links for more information about each):<br />
<br />
# <u>Research questions 1 and 2: Image indexing efficiency and effectiveness</u><br />
## '''Proof of concept (spring 2022) - Successfully ingested play-by-play data for over 12,500 college football game from 2005 - 2021''': During the spring 2022 semester, we completed the design of an high-throughput ETL pipeline and successfully evaluated the pipeline by loading play-by-play statistical data for 17 college football seasons (2005-2021). <b>This load resulted in a [https://sportsdamkg.slis.ua.edu/sparql/ SPARQL queriable] graph database of over 2.5 million entities representing about 12,500 games and over 2.1 million plays that were run during those games.</b> This successful database load is a scaled up proof of concept for applying linked data technologies to organization of information challenges. <b>Sample SPARQL queries</b>:<br />
### [https://tinyurl.com/2k3j29ch All plays from the 2020 Alabama Crimson Tide football season with their corresponding game clock and wall clock time]<br><br />
### [https://tinyurl.com/yatkv4rt All plays from the 2018 Michigan Wolverine football season with their corresponding game clock and wall clock time]<br><br />
### [https://tinyurl.com/ybmq2gw9 All plays from the 2010 Alabama Crimson Tide football season with their corresponding game clock time]<br />
### [https://tinyurl.com/y9hs6wh9 All plays from the 2019 SEC football season (all teams) with their corresponding game clock and wall clock time]<br />
### [https://tinyurl.com/y7oyjllp Rushing touchdowns run for over 50 yards in 2021 SEC season games (all teams)]<br />
## '''Pilot study (spring 2019) - Successfully ingested play-by-play data for [https://wikibase.slis.ua.edu/wiki/2018-19_Academic_Year_Research_Report all 15 games from the 2017 Alabama Crimson Tide football season]''': We have completed the semantic indexing of every single play that occurred in all 15 games from the 2017 Alabama Crimson Tide football season using a semantic indexing method that incorporated JSON-formatted Web-accessible statistical play-by-play datasets into a semantic indexing process via the creation of a semi-automated data processing pipeline. This effort was partially funded by two 2018 grants that we received; see chronology below. '''''See [[2018-19 Academic Year Research Report]] for detailed documentation and software demonstration pertaining to the accomplishment this milestone.'''''<br />
## '''Pilot study of historical games (1) (spring 2020) - Successfully ingested partial play-by-play data from [https://wikibase.slis.ua.edu/wiki/2019-20_Academic_Year_Research_Progress_Report#Semantic_Indexing_of_2_Games_from_1992_Alabama_Football_Season 2 games from the 1992 Alabama Crimson Tide football season]''': We have completed RGC-grant funded work (see 2018 entry in chronology below) that allowed us to investigate the recovery of play-by-play data from the historical record. For games in 1992, the historical record consisted of typed play-by-play datasets requiring manual transcription by volunteers, which allowed us to investigate the inclusion into our data processing pipeline of a crowdsourcing method for historical data transcription. '''''See [[2019-20 Academic Year Research Progress Report]] for detailed documentation and software demonstration pertaining to the accomplishment this milestone.'''''<br />
## '''Pilot study of historical games (2) (spring 2020) - Successfully ingested partial play-by-play data from [https://wikibase.slis.ua.edu/wiki/2019-20_Academic_Year_Research_Progress_Report#Semantic_Indexing_of_2_Games_from_1961_Alabama_Football_Season 2 games from the 1961 Alabama Crimson Tide football season]''': As part of the above mentioned RGC grant, we also investigated games from the 1961 season. For games from 1961, there were no historical records available containing play-by-data. However, there are other documentary sources of such data, including journalistic newspaper accounts of those games. In our study, we investigated the inclusion into our data processing pipeline of a crowdsourcing method for historical data-extraction from newspaper articles. We chose to use articles published in the Tuscaloosa News available online. '''''See [[2019-20 Academic Year Research Progress Report]] for detailed documentation and software demonstration pertaining to the accomplishment this milestone.'''''<br />
## PLEASE NOTE: '''One additional game from the 1961 Alabama Crimson Tide football season''': When working with the 1961 season, we came across a fully reconstructed game, the "Iron Bowl" game against Auburn on December 2, 1961 that had been converted into a television production using digitized coaches film and historical accounts maintained at the Paul W. Bryant Museum. Based on the availability of this digitized full-game content, we were able to index every play of that game as if it were a game from the 2017 season (described above). '''''See [[2019-20 Academic Year Research Progress Report]] for detailed documentation and software demonstration pertaining to this game.'''''<br />
# <u>Research question 3: Integration</u>: After completing the semantic indexing of individual plays from the three seasons of Alabama Crimson Tide football as described above, we were able to successfully evaluate the querying of plays as if they were all in the same semantic database. Here are example SPARQL queries demonstrating this capability. ('''PLEASE NOTE: To run each query, click on the Blue Arrow icon in lower left portion of screen after clicking on links below'''):<br />
## [https://tinyurl.com/r2o3h23 All rushing touchdowns that went for over 50 yards] during the 3 seasons of Alabama Crimson Tide football in our database (just those for which there are [https://tinyurl.com/s5nzqgq video clips])<br />
## [https://tinyurl.com/v459wr2 All interceptions returned for 15 or more yards] during the 3 seasons of Alabama Crimson Tide football in our database (just those for which there are [https://tinyurl.com/u3zhzm2 video clips])<br />
## [https://tinyurl.com/s7td74m All successful field goals from 30 yards or more] during the 3 seasons of Alabama Crimson Tide football in our database (just those successful field goals for which there are [https://tinyurl.com/ts7qvlx video clips])<br />
# <u>[https://wikibase.slis.ua.edu/wiki/Plays_with_Example_UA_Images_May_2019 Research question 4: Identification]</u>: Crimson Tide Photos, a unit in UA Athletics, provided us with a random sample of born digital photos that were taken during games from the 2017 Alabama Crimson Tide football season, and we were able to identify each image in terms of the play context and the game statistical situation at the time that each photo was taken. ('''''See [[Plays with Example UA Images May 2019]] for documentation of this milestone.''''')<br />
<br />
===Conventional Indexing Era: Omeka and ContentDM Software (2008 to 2018)===<br />
During this period, SLIS students in my annual spring semester Metadata course (LS 566) benefited from the application of my research as I was able to use it to inform my teaching. Specifically, my research served as the basis for their course project work, which involved the application of indexing theories by way of applying the standard/conventional approach to the indexing of a set of images. These images were provided by Ken Gaddy, Director of the Paul W. Bryant Museum, and they were both digitized black and white photos of Alabama Crimson Tide football games from the 1975 season and also born digital color images from the 2010 National Championship game.<br />
<br />
The indexing of these images was accomplished using two different software applications. <br />
<br />
# [https://omeka.org/ Omeka software] (2011-2018): Here is a representative example of the end result of the last of such indexing projects from the [http://omeka.slis.ua.edu/ls566-spring2018/bama-football-images/items/browse Spring 2018 semester of this course].<br />
# [https://www.oclc.org/en/contentdm.html ContentDM] (2008-2010)<br />
<br />
==Research-related Accomplishments by Year==<br />
<br />
<b><i>See also [[Publications and Presentations]]</i></b><br />
<br />
The entries below are arranged reverse chronologically:<br />
<br />
===2023===<br />
Article published in a Nature Portfolio journal:<br />
<br />
Liu, Ho., Aderon, C., Wagon, N., Bamba, A. L., Li, X., Liu, Hu., MacCall, S. & Gan, Y. (2023). Automated player identification and indexing using two-stage deep learning network. Scientific Reports, 13, article number: 10036 (available open access: https://doi.org/10.1038/s41598-023-36657-5).<br />
<br />
===2022===<br />
During the spring 2022 semester, we completed the design of an high-throughput ETL pipeline and successfully evaluated the pipeline by loading play-by-play statistical data for 17 college football seasons (2005-2021). This load resulted in a [https://sportsdamkg.slis.ua.edu/sparql/ SPARQL queriable] graph database (using Wikibase software) of over 2.5 million entities representing about 12,500 games and over 2.1 million plays that were run during those games. This successful database load is a scaled up proof of concept for applying linked data technologies to organization of information challenges.<br><br />
<br>Sample SPARQL queries:<br><br />
* [https://tinyurl.com/yc25vjls All plays from the 2020 Alabama Crimson Tide football season with their corresponding game clock and wall clock time]<br><br />
* [https://tinyurl.com/yatkv4rt All plays from the 2018 Michigan Wolverine football season with their corresponding game clock and wall clock time]<br><br />
* [https://tinyurl.com/ybmq2gw9 All plays from the 2010 Alabama Crimson Tide football season with their corresponding game clock time]<br />
* [https://tinyurl.com/y9hs6wh9 All plays from the 2019 SEC football season (all teams) with their corresponding game clock and wall clock time]<br />
* [https://tinyurl.com/y7oyjllp Rushing touchdowns run for over 50 yards in 2021 SEC season games (all teams)]<br />
<br />
===2021===<br />
<br />
Design of ETL pipeline begins by Huapu Liu:<br />
* Python and R scripting for accessing and transforming game datasets from [https://api.collegefootballdata.com/api/docs/?url=/api-docs.json College Football Data's API]<br />
* Optimized data upload speed to our Wikibase instance via API<br />
<br />
Continuing work with Dr. Gan's group in the UA Department of Electrical and Computer Engineering. Completion of senior project work: Nguyen Nguyen, Jeff Reidy, and Noah Wagnon. "Final Presentation: Automated Jersey Number Recognition". Senior design team presentation for EECE 494.407 for 2020-21 academic year. [http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/494-407_final_pres_-_for_dr_gan.pdf PDF slidedeck]<br />
<br />
===2020===<br />
<br />
MacCall, S.L. (2020). Systems and methods for digital asset organization. U.S. Patent number 10,534,812. <br />
<br />
Partnership established with [https://eng.ua.edu/people/yu-gan/ Dr. Yu Gan], Assistant Professor of Electrical and Computer Engineering at the University of Alabama. Dr. Gan is an expert in digital image processing, and he is working with masters student Alexander Ramey to automatically detect player numbers in order to extract player participation data from born digital and digitized historical video: The 2017 season [https://wikibase.slis.ua.edu/wiki/2018_Alabama_versus_Georgia_CFP_National_Championship_Football_Game_Presented_by_AT%26T National Championship game] and the 1961 season [https://wikibase.slis.ua.edu/wiki/1961_Alabama_versus_Auburn_Football_Game Iron Bowl].<br />
<br />
MacCall, S.L., Liu, H., & Anderson, C.M. (2020). Statistical data recovery from historical documentation of Alabama football games using Wikibase as a repository. Interactive demonstration accepted for Connecting Collections as Data: Transforming Communities, Sharing Knowledge, and Building Networks with International GLAM Labs, Washington, DC.<br />
<br />
MacCall, S.L. (2020). Data-driven semantic DAM indexing incorporating statistical play-by-play game logs: A linked data application using Wikibase from the 2017 football season of the Alabama Crimson Tide. Conference paper accepted for presentation at the 2020 LD4 Conference on Linked Data in Libraries, College Station, TX.<br />
<br />
Anderson, C.M., Liu, H., & MacCall, S.L. (2020). Crowdsourcing in a semantic indexing workflow for efficiently organizing historical multimedia sports collections. Poster accepted for the 2020 Annual Meeting of the Alabama Library Association, Birmingham, AL. <br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Wikibase software, as the basis for the major indexing project in my brand new spring 2020 Linked Data course (LS 590)<br />
<br />
===2019===<br />
<br />
MacCall, S.L., Liu, H., & Anderson, C.M. (2019). How much statistical data can be recovered from Alabama football history? Piloting a crowdsourced approach using Wikibase as data repository. Conference paper presented at 2019 Digitorium Digital Humanities Conference, Tuscaloosa, AL. [UA Institutional Repository deposit: [https://ir.ua.edu/handle/123456789/6574 https://ir.ua.edu/handle/123456789/6574]]<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with OmekaS software, as the basis for the major metadata indexing project in my spring 2019 Metadata course (LS 566)<br />
<br />
===2018===<br />
<br />
MacCall, S.L. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 2: Developing SLIS research capacity for key linked open data technologies. University of Alabama School of Library and Information Studies Research Fund -$1,000. Funded. <br />
<br />
MacCall, S.L., & Bott, G. (2018). Investigation of a data-driven indexing method for multimedia asset collections in sports: Phase 1: How much data can be recovered from Alabama football history? University of Alabama Office of Research and Development Research Grants Committee Level 1 Program - $6,000. Funded <br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with OmekaS software, as the basis for the major metadata indexing project in my spring 2018 Metadata course (LS 566)<br />
<br />
===2017===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with OmekaS software, as the basis for the major metadata indexing project in my spring 2017 Metadata course (LS 566)<br />
<br />
===2016===<br />
<br />
MacCall, S.L., McMillan, D.J., Vargo, C.J., Bradley, S.B., & Aversa, E.A. (2016). Efficiency, integration, interoperability: A 21st century approach to organizing sports digital assets for all libraries. Knight Foundation’s News Challenge for Libraries: How Might Libraries Serve 21st Century Information Needs? - pre-budget grant submission. Not funded.<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2016 Metadata course (LS 566)<br />
<br />
===2015===<br />
<br />
MacCall, S.L. (Filed December 15, 2015). Systems and methods for digital asset organization. U.S. Utility Patent Application number 14/971,463.<br />
<br />
MacCall, S.L., Vargo, C.J., Bradley, S.B., & Aversa, E.A. (2015). Development of a novel digital asset organizing method in sports. National Science Foundation - Small Business Innovation Research (SBIR) Phase I Grant - $225,000 ($74,925 sub-award to University of Alabama). Not funded. <br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2015 Metadata course (LS 566)<br />
<br />
===2014===<br />
<br />
MacCall, S.L. [Chief Scientist for MaxOrg, LLC], Aversa, E.A. [CEO for MaxOrg, LLC], & McMillan, D.J. [Technology Officer for MaxOrg, LLC]. (2014, 2015, 2016). Crimson Canvas - MaxOrg, LLC. Program participation: Commercial Development of Faculty Developed UA Intellectual Property sponsored by Alabama Innovation and Mentoring of Entrepreneurs (AIME).<br />
<br />
MaxOrg, LLC formed as a faculty-led startup to contribute to the commercial development of UA intellectual property (see 2020 and 2015 entries above for patent issued and patent filed data respectively)<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2014 Metadata course (LS 566)<br />
<br />
===2013===<br />
<br />
MacCall, S.L. & Gaddy, K. (2013). Optimal organizing of digital images in sports: A project of the Paul W. Bryant Museum and UA SLIS. Presented at the 2013 University of Alabama Program in Sports Communication Sports Symposium, Tuscaloosa, AL. [Slideshare: [http://tinyurl.com/k875g5j http://tinyurl.com/k875g5j]]<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2013 Metadata course (LS 566)<br />
<br />
===2011===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with Omeka Classic software, as the basis for the major metadata indexing project in my spring 2011 Metadata course (LS 566)<br />
<br />
===2010===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with ContentDM software, as the basis for the major metadata indexing project in my spring 2010 Metadata course (LS 566)<br />
<br />
===2009===<br />
<br />
Used the Paul W. Bryant Museum image sets provided in 2008 by Ken Gaddy, along with ContentDM software, as the basis for the major metadata indexing project in my spring 2009 Metadata course (LS 566)<br />
<br />
===2008===<br />
<br />
Partnership begun with Ken Gaddy, director of the Paul W. Bryant Museum. Ken provided my students and me with a set of digitized black and white photos from the 1975 Alabama Crimson Tide football season and a set of born digital color images from the 2010 National Championship game. These images served as the training set for students in my LS 566 course on Metadata every year since 2008. Equally importantly, my own study of these images was instrumental in the development of my research program and subsequently led to the research output reported here.<br />
<br />
Adopted use of ContentDM image repository software for the metadata indexing project in my LS 566 Metadata course at SLIS.<br />
<br />
==Team Members and Major Supporters==<br />
<br />
# Ken Gaddy, Director, Paul W. Bryant Museum (retired)<br />
# Dr. Greg Bott, Assistant Professor, UA Culverhouse College of Business (co-author of RGC grant)<br />
# David McMillan, Executive Director, Enterprise Development Application Support and MaxOrg, LLC (led install of Wikibase software and its software customization to this project)<br />
# Dr. Yu Gan, Assistant Professor, UA Department of Electrical and Computer Engineering (digital image processing)<br />
# Huapu Liu, MLIS, my former Graduate Research Assistant and frequent co-author (provided indispensable support from the very beginning of the Wikibase phase of this research project when we were faced with an empty database!)<br />
# C. Melissa Anderson, my current Graduate Research Assistant and co-author (project manager for RGC grant)<br />
# Christina Schultz-Richert, MLIS graduate (worked to extend the football related model to Public Television episode transcripts)<br />
# Dr. Elizabeth Aversa,retired SLIS Director and MaxOrg, LLC</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=280002Main Page2025-11-29T19:41:56Z<p>Smaccall: /* Projects */</p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
<i>For current information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu]</i> <br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[https://ir.ua.edu/bitstreams/69a9a746-9003-4996-a5ec-d4597f1e762f/download presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/ppt-final_liu-maccall-lewis-wissenpresoconf-presentation-2021.pdf presentation slides]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology. (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/finalposter_2021asistannualmeeting.pdf Poster PDF]</b>).<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Consultant<br><br />
<i>formerly</i><br><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS, PhD<br></b><br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=280001Main Page2025-11-29T19:39:35Z<p>Smaccall: /* Projects */</p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
<i>For current information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu]</i> <br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[https://ir-api.ua.edu/api/core/bitstreams/69a9a746-9003-4996-a5ec-d4597f1e762f/content?authentication-token=eyJhbGciOiJIUzI1NiJ9.eyJlaWQiOiI4OTU2MGI2OC1lNDAyLTRlNjItYTQ0Ni0zZGVjNTk5OWRiMzAiLCJzZyI6W10sImF1dGhlbnRpY2F0aW9uTWV0aG9kIjoicGFzc3dvcmQiLCJleHAiOjE3NjQ0NDUxMDl9.dSdbD07JZvo9UoICBQR1FAvDpWn1KqBVCNPlAHXUZUE presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/ppt-final_liu-maccall-lewis-wissenpresoconf-presentation-2021.pdf presentation slides]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology. (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/finalposter_2021asistannualmeeting.pdf Poster PDF]</b>).<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Consultant<br><br />
<i>formerly</i><br><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS, PhD<br></b><br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=280000Main Page2025-11-28T19:18:59Z<p>Smaccall: /* Leadership */</p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
<i>For current information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu]</i> <br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/2022_asist_ai_workshop_presentation-liu_and_maccall-final.pdf presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/ppt-final_liu-maccall-lewis-wissenpresoconf-presentation-2021.pdf presentation slides]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology. (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/finalposter_2021asistannualmeeting.pdf Poster PDF]</b>).<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Consultant<br><br />
<i>formerly</i><br><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS, PhD<br></b><br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=279999Main Page2025-11-28T19:17:27Z<p>Smaccall: </p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
<i>For current information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu]</i> <br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/2022_asist_ai_workshop_presentation-liu_and_maccall-final.pdf presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/ppt-final_liu-maccall-lewis-wissenpresoconf-presentation-2021.pdf presentation slides]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology. (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/finalposter_2021asistannualmeeting.pdf Poster PDF]</b>).<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS, PhD<br></b><br />
<br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=279998Main Page2025-11-28T19:16:17Z<p>Smaccall: </p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
<i>For current information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu]</i> <br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/2022_asist_ai_workshop_presentation-liu_and_maccall-final.pdf presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/ppt-final_liu-maccall-lewis-wissenpresoconf-presentation-2021.pdf presentation slides]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology. (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/finalposter_2021asistannualmeeting.pdf Poster PDF]</b>).<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS<br></b><br />
Doctoral Student<br><br />
College of Communication and Information Sciences<br><br />
University of Alabama<br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=279997Main Page2025-11-28T19:15:54Z<p>Smaccall: </p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
For current information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu] <br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/2022_asist_ai_workshop_presentation-liu_and_maccall-final.pdf presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/ppt-final_liu-maccall-lewis-wissenpresoconf-presentation-2021.pdf presentation slides]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology. (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/finalposter_2021asistannualmeeting.pdf Poster PDF]</b>).<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS<br></b><br />
Doctoral Student<br><br />
College of Communication and Information Sciences<br><br />
University of Alabama<br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=279996Main Page2025-11-28T19:15:15Z<p>Smaccall: </p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
<i>This website reflects research and development activities undertaken by the Research Group between 2018 and 2024.</i><br />
<br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
For information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@retiree.ua.edu smaccall@retiree.ua.edu] <br><br><br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/2022_asist_ai_workshop_presentation-liu_and_maccall-final.pdf presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/ppt-final_liu-maccall-lewis-wissenpresoconf-presentation-2021.pdf presentation slides]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology. (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/finalposter_2021asistannualmeeting.pdf Poster PDF]</b>).<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS<br></b><br />
Doctoral Student<br><br />
College of Communication and Information Sciences<br><br />
University of Alabama<br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=279995Main Page2025-11-28T19:12:40Z<p>Smaccall: </p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
<br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
For information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@ua.edu smaccall@ua.edu] <br><br><br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/2022_asist_ai_workshop_presentation-liu_and_maccall-final.pdf presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/ppt-final_liu-maccall-lewis-wissenpresoconf-presentation-2021.pdf presentation slides]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology. (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/finalposter_2021asistannualmeeting.pdf Poster PDF]</b>).<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS<br></b><br />
Doctoral Student<br><br />
College of Communication and Information Sciences<br><br />
University of Alabama<br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=279994Main Page2025-11-28T19:11:08Z<p>Smaccall: </p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
Established in August 2018<br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
For information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@ua.edu smaccall@ua.edu] <br><br><br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/2022_asist_ai_workshop_presentation-liu_and_maccall-final.pdf presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/ppt-final_liu-maccall-lewis-wissenpresoconf-presentation-2021.pdf presentation slides]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology. (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/finalposter_2021asistannualmeeting.pdf Poster PDF]</b>).<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS<br></b><br />
Doctoral Student<br><br />
College of Communication and Information Sciences<br><br />
University of Alabama<br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Researchers during 2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, PhD<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Main_Page&diff=279993Main Page2025-11-28T18:55:35Z<p>Smaccall: </p>
<hr />
<div><br />
<font size="+2"><b>Welcome to the Linked Data Research Group!</b></font><br><br />
Established in August 2018<br><br><br />
[https://slis.ua.edu/ School of Library and Information Studies]<br><br />
[https://cis.ua.edu/ College of Communication and Information Sciences]<br><br />
University of Alabama<br />
<br />
For information, email [https://smaccall.people.ua.edu Dr. Steven L. MacCall] at [mailto:smaccall@ua.edu smaccall@ua.edu] <br><br><br />
<br />
====Publications, Presentations, Background Readings====<br />
* [[Publications and Presentations]]<br />
* Selected background [[Reading Lists]]<br />
<br />
====Projects====<br />
<br />
# <b>Sports DAM Knowledge Graphs resulting from the semantic indexing of time-based media collections</b>: Our longest standing project, we are investigating precision queries of collections of still images and video clips that document game action in sports. Primary data source for semantic indexing method are play-by-play datasets. Precision queries incorporate game situation variables. Additional information:<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research Overview of Project]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research-related_Accomplishments_by_Year Research-related Accomplishments by Year]<br />
## [https://wikibase.slis.ua.edu/wiki/Chronology:_Data-driven_Sports_Image_Indexing_Research#Research_Result_Highlights_and_Links_to_Working_Software_Demonstrations Research Result Highlights and Links to Working Software Demonstrations]<br />
## [https://tinyurl.com/y5wcxqen Video of presentation] by Dr. MacCall to the 2020 Linked Data for Libraries (LD4L) conference<br />
# <b>Philology Graphs resulting from the semantic indexing of text collections</b>: The rapid growth in the number of digital texts has posed challenges for librarians to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to create semantic descriptions for these resources aiming at more efficient and effective granular access to specific textual content. Ongoing work includes the challenge of computationally recovering the typographical logic of book indexes that is lost when standard page-level OCR scanning techniques are deployed on index pages in historical books hosted at the HathiTrust Digital Library. Recovering typographical logic would aid in the provision of granular access to digital texts through improving textual navigation. Additional information:<br />
## Liu, H., & MacCall, S.L. (2022). Historical Text Datafication and Loss: Computational Recovery of Typographical Layout Logic on an RDF Graph Featuring ML Methods. Conference paper presented at the 2022 ASIS&T Annual Meeting Pre-conference AI Workshop: [https://asistaiworkshop.web.illinois.edu/program-2022/ AI in the Real World: Strengthening Connections Between LIS Research and Practice] (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/2022_asist_ai_workshop_presentation-liu_and_maccall-final.pdf presentation slides and speaker notes]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Knowledge Graph as Navigational Paratext: Returning Structural Semantics to a Closed System Index through the Computational Recovery of the Typographical Logic of Digitized Historical Book Indexes. Conference paper presented at Wissensorganisation 2021, biennial conferences of the German chapter of the International Society of Knowledge Organization (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/ppt-final_liu-maccall-lewis-wissenpresoconf-presentation-2021.pdf presentation slides]</b>).<br />
## Liu, H., MacCall, S.L., & Lewis, N.A. (2021). Exploring the computational recovery of the typographical logic of book indexes as paratext for improving navigation within digitized historical texts using semantic methods. Poster presented at the 2021 annual meeting of the American Society for Information Science and Technology. (<b>[http://smaccall.people.ua.edu/uploads/1/0/1/9/101978326/finalposter_2021asistannualmeeting.pdf Poster PDF]</b>).<br />
# <b>Documentary Series Knowledge Graphs resulting from the semantic indexing of time-based documentary media collections (series)</b>: This our most recently established project as we seek to generalize our semantic indexing method from sports image and video clip collections to collections ("series") of documentaries and other broadcast genres. Objectives:<br />
## Our goal is to investigate models for the semantic indexing of broadcast series for granular access and semantic integration across the linked data cloud using data generated by ML/AI computational methods applied to the documentaries of the series.<br />
## Our purpose is to extend and enhance the existing relationships of the AAPB and WGBH with the UA [https://slis.ua.edu/ School of Library and Information Studies] to benefit both EBSCO Scholars and SLIS students generally.<br />
## Additional information: [[Data-driven Semantic Indexing of Time-based Broadcast Media Series]]<br />
<br />
====Leadership====<br />
<b>[https://smaccall.people.ua.edu Steven L. MacCall, PhD]<br></b><br />
Associate Professor<br><br />
School of Library and Information Studies<br><br />
University of Alabama<br />
<br />
<b>Huapu Liu, MLIS<br></b><br />
Doctoral Student<br><br />
College of Communication and Information Sciences<br><br />
University of Alabama<br />
<br />
====Mission====<br />
Our digital libraries research mission is to apply linked data methods to investigate the <b>data-driven semantic indexing of text and time-based media collections</b> (born digital and digitized historical) to facilitate the precision search for attribute-bearing collection items, item parts, and other granular components via SPARQL queries and URL-based locators. We are using a [https://osf.io/mx6uk/wiki/home/ Collections as (Meta)data] and [https://www.theindexer.org/files/23-1/23-1_023.pdf closed-system indexing approach] as we investigate various methods for indexing collections based on semantically mapping properties and extracted named entities and features that have been either manually created (e.g., book indexes or video logs) or computationally detected (e.g., among others, [https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-docs/ Microsoft Azure Video Analyzer for Media]). The result of our semantic indexing method are SPARQL queriable RDF knowledge graphs that are hosted on a local Wikibase instance managed by the Office of Information Technology (OIT) at the University of Alabama.<br />
<br />
====General Research Questions====<br />
There are two overarching empirical research questions that we are pursuing:<br />
# <b>Scalability of semantic indexing method</b>: Data-driven aspects of our semantic indexing method are key to addressing scalability issues. Following semantic uplift methods, which involve ETL pipeline development in which RDF triples are generated during the transform phase, we have had demonstrable success using R and Python scripting methods coupled with semantic data models and direct API access to our Wikibase instance facilitate efficient transformed data upload at scale.<br />
# <b>Queriability of semantic indexing results</b>: The semantic indexing of both text and time-based media collections facilitates precision queries (compare a book index (more precise) to a table of contents (less precise)). When RDF triples are loaded to a knowledge graph, the resulting triplestore can be queried with precision using SPARQL. But are these precise queries useful?<br />
<br />
====Current Researchers (2022-23 Academic Year)====<br />
# [http://smaccall.people.ua.edu/ Dr. Steven L. MacCall]<br />
# Huapu Liu, CIS doctoral candidate<br />
# Duncan McCaskill, CIS doctoral student<br />
# William Blackerby, SLIS master's student<br />
<br />
====Affiliate Researchers====<br />
# <b>[https://www.stevens.edu/profile/ygan5 Dr. Yu Gan]</b> from Stevens Institute of Technology for digital image processing<br />
# <b>[https://www.ua.edu/news/ua-theme-expert/gregory-bott/ Dr. Greg Bott]</b> from the UA Department of Information Systems, Statistics and Management Science for database design and Python programming guidance<br />
<br />
====Previous SLIS Students====<br />
# <b>Nicole Lewis, SLIS MLIS graduate</b>: For her Fall 2020 and Spring 2021 directed research studies, Nicole applied what she learned in her Linked Data course in Summer 2020. She worked with the HathiTrust Digital Library Research Center's [https://analytics.hathitrust.org/datasets Extracted Features Datasets]. Nicole was instrumental in the initial design of the "transform" portion of an ETL pipeline using back-of-the-book indexes as a source for named entities for page-level subject access.<br />
# <b>C. Melissa Anderson, SLIS MLIS graduate</b>: Melissa served as Dr. MacCall's graduate assistant during the 2019-20 academic year assisting immeasurably in project development, management, and student teaching role in various technology-intensive SLIS courses taught by Dr. MacCall. Melissa also served as research project manager for an RGC grant for which she was instrumental in preparing materials for a Zooniverse-based crowdsourcing effort and in recruiting and supervising volunteer transcribers. This led to her inclusion as a co-author on a [https://ir.ua.edu/handle/123456789/6574 DH conference presentation].<br />
# <b>Jessica Camano, SLIS MLIS graduate</b>: For her Fall 2020 directed research study, Jessica is investigating a "red letter" problem that emerged from our work in the summer Linked Data course in which our subject terms remain without MediaWiki sitepages. Resolution of this problem involved researching the National Library of Medicine's linked data service ([https://id.nlm.nih.gov/mesh/ Medical Subject Headings RDF]) to download metadata about MeSH entries as RDF triples.<br />
# <b>David Roby, SLIS MLIS graduate</b>: For his Fall 2020 directed research study, David is helping us investigate a research problem related to scaling the data management methods and ETL pipeline for our ongoing research into the semantic indexing of digital images and video clips documenting game action in sports.<br />
# <b>Christine Schultz-Richert, SLIS MLIS graduate</b>: For her Summer 2019 directed research study, Christine investigated the semantic enhancement of transcripts from a 1957 [https://americanarchive.org/special_collections/net-catalog National Educational Television (NET)] program (A Look at the Indian's Future) using linked data methods.<br />
<br />
====Special Thanks====<br />
Special thanks to David J. McMillan, former Executive Director for Enterprise Development & Application Support in the UA Office of Information Technology (OIT)</div>Smaccallhttps://wikibase.slis.ua.edu/w/index.php?title=Item:Q6670&diff=278756Item:Q66702021-07-25T15:32:55Z<p>Smaccall: /* wbcreateclaim-create:1| */ Property:P192: https://www.youtube.com/embed/A-vYhg-1BZ4?start=2498&end=2535, #quickstatements; #temporary_batch_1627227130302</p>
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