Tutorial 1: LLMs for Semantic Web Query

Starts at
Thu, Nov 9, 2023, 09:00 South Korea Time
( 09 Nov 23 00:00 UTC )
Finishes at
Thu, Nov 9, 2023, 10:30 South Korea Time
( 09 Nov 23 01:30 UTC )
Room 209
Inkyung Choi

The emergence of Large Language Models like GPT-4 offers unprecedented capabilities in understanding human intent and generating text. This tutorial explores the intersection of LLMs and semantic web applications, focusing on how these models can automatically generate queries that adhere to metadata standards. Participants will engage in hands-on exercises that demonstrate the integration of LLMs into a sample semantic web application. This session will offer conceptual understanding and practical skills for metadata practitioners, developers, and researchers. The aim is to enable attendees to leverage the capabilities of LLMs in enhancing semantic web applications. Target audience: Metadata practitioners, developers, researchers, and those interested in Large Language Models

Expected learning outcomes:

  • Understand LLMs and their capabilities.
  • Gain hands-on experience and learn to generate metadata-compliant queries using LLMs.
  • Discuss potential applications and limitations of LLMs in the semantic web.

Tutorial style: Presentation, demonstration, hands-on practice, discussion and Q&A

Prior knowledge required:

  • Basic familiarity with semantic web technologies, such as RDF or SPARQL
  • Some basic Python programming skills

Participants are recommended to have: A dual-monitor setup or two computers to more easily follow along with hands-on exercises while also watching the presentation


  • Inkyung Choi


    Inkyung Choi is an associate research scientist for the OCLC Research with a focus on data science and metadata research, and community engagement on next generation cataloging practices. She has developed and taught courses in a field of Information Organization and Knowledge Organization including topics such as cataloguing, linked data processing, and taxonomy/thesaurus construction during her time as a Teaching assistant professor at the University of Illinois Urbana-Champaign. She earned her PhD in Information science from University of Wisconsin-Milwaukee and her MLIS from Syracuse University.


  • Yinlin Chen

    Virginia Tech University Libraries

    Dr. Yinlin Chen is an Assistant Professor and Assistant Director at Virginia Tech University Libraries. Chen holds a Ph.D. in Computer Science and Application from Virginia Tech and M.S. and B.S. degrees from National Tsing Hua University in Taiwan. His research interests span Digital Libraries, Machine Learning, and Natural Language Processing. At Virginia Tech, he teaches an Introduction to AI course, contributing to the education of future AI professionals. His professional focus is on bridging the divide between human and artificial systems through advanced AI techniques.