OntologySummit2024

Ontology Summit 2024

The Ontology Summit is an annual series of events that involves the ontology community and communities related to each year's theme chosen for the summit. The Ontology Summit was started by Ontolog and NIST, and the program has been co-organized by Ontolog and NIST along with the co-sponsorship of other organizations that are supportive of the Summit goals and objectives.

Purpose

As part of Ontolog’s general advocacy to bring ontology science and related engineering into the mainstream, we endeavor to facilitate discussion and knowledge sharing amongst stakeholders and interested parties relevant to the use of ontologies. The results will be synthesized and summarized the form of the Ontology Summit 2024 Communiqué, with expanded supporting material provided on the web and in journal articles.

Process and Deliverables

Similar to our last seventeen summits, this Ontology Summit 2024 will consist of virtual discourse (over our archived mailing lists), virtual presentations and panel sessions as part of recorded video conference calls. As in prior years the intent is to provide some synthesis of ideas and draft a communique summarizing major points. This year will begin with a Fall Series in October and November; the main summit will begin in February.

Meetings are at Noon US/Canada Eastern Time on Wednesdays and last about an hour.

Fall Series on Ontologies and Large Language Models: Related but Different

Fall Series Co-Chairs: Andrea Westerinen and Mike Bennett

Neuro-Symbolic Techniques for and with Ontologies and Knowledge Graphs

The summit will survey current techniques that combine neural network machine learning with symbolic methods, especially methods based on ontologies and knowledge graphs.

Ontologies are representations of a knowledge domain. They define the concepts, relationships, properties, axioms and rules within that domain, providing a framework that enables a deep understanding of that subject area. Knowledge graphs are structured representations of semantic knowledge that are stored in a graph. Ontologies and knowledge graphs are used to enable machine reasoning and semantic understanding, allowing a system to draw inferences and to derive new information and relationships between entities.

Neural network and other machine learning models, such as LLMs, are trained on large corpora, learning the patterns and connections between words and images. Hence, although their “knowledge base” is broad, it is also sometimes incorrect and/or biased, and don't explicitly understand the semantics or relationships in that content.

Consequently, neural network and traditional AI techniques are complementary. The Fall Series of the summit explored the similarities and distinctions between ontologies and LLMs, as well as how they can be used together. The Main Summit Series will examine the more general topic of neuro-symbolic techniques, especially how one can leverage the complementary benefits of neural networks and of ontologies and knowledge graphs.

Main Series Tracks

• Track A. Foundations and Architectures
• Track B. Large Language Models, Ontologies and Knowedge Graphs
• Track C. Applications
• Track D. Risks and Ethics

Schedule

Fall Series

• 4 October 2023 Kickoff/Overview by Andrea Westerinen and Mike Bennett
• 11 October 2023 Deborah McGuiness Knowledge Graphs in Today’s Evolving Landscape (and Beyond)
  • “Many remaining challenges for Large Language Models and Generative AI align with the strengths of Knowledge Graphs and Semantic AI”
• 18 and 25 October 2023 Technical assistance and hybrid systems
  • Kurt Cagle ChatGPT (LLMs) vs. Knowledge Graphs and Nine ChatGPT Tricks for Knowledge Graph Workers
  • Tony Seale TBA
  • Evren Sirin, Stardog How AI Uses Stardog and Stardog Voicebox FAQ: How LLM, Generative AI, and Knowledge Graphs are the Future of Data Management
  • Yuan He DeepOnto, a package for ontology engineering with deep learning
• 1 November 2023 Dive into information extraction via hybrid systems
  • Andrea Westerinen Using LLMs to map text to an ontology
    • Open source to be available at https://github.com/ontoinsights/deep_narrative_analysis)
  • Prasad Yalamanchi Lead Semantics Product Demo
• 8 November 2023 Broader thoughts
  • Anatoly Levenchuk, Hybrid reasoning and scope of knowledge, what is beyond ontologies?
  • John Sowa and Arun Majumdar LLMs, Ontologies, formal systems, and method
• 15 November 2023 Discussion and Synthesis, including questions for the full summit

Resources

Ontology Summit YouTube Channel