Large Language Models for Knowledge Graph and Ontology
Engineering
2024 AAAI Fall Symposium
Large Language Models (LLMs) and Knowledge Graphs (KGs) are highly trending. The interplay between these two technologies can go both ways, but the two directions are quite different in approach. This symposium specifically focuses on how LLMs can be used as tools to augment the extant capacity for ontology and knowledge graph engineering. Knowledge Graph Engineering (KGE) and Ontology Engineering (OE – together KG/OE) challenges in particular have to do with the (to date still) high involvement of humans and human expert in the KG/OE life cycle, including creation/modeling, alignment, evolution, reusability (from both ontological commitment and accessibility perspectives). The KG/OE communities have made steady progress in the past 20 years, but only now with LLMs, key KG/OE challenges appear to become addressable at scale.
The goal of this symposium to focus and coordinate research. We wish to create a space and foundational community for the sharing of ideas for prompt engineering, fine-tuning, neurosymbolic approaches, quality control, and human-in-the-loop methods: all with LLMs for OE/KGE.
Topics include, but are not limited to:
Format of the Symposium: The program will consist of presentations of accepted full papers, posters, lightning talks, keynotes, and significant time for panel and plenary discussions.
Submission of papers:
Submissions are to be made via the official AAAI Symposium Easychair submission portal at https://easychair.org/my/conference?conf=fss24. You must choose from the appropriate symposia from the available tracks.
Deadline for submission of papers: July 31,
2024.
Symposium Committee:
For more information, see https://kastle-lab.github.io/llms-and-kg-engineering/
-- Pascal Hitzler Lloyd T. Smith Creativity in Engineering Chair Director, Center for AI and Data Science CAIDS Director, Inst. for Digital Agriculture and Adv. Analyt. ID3A Kansas State University http://www.pascal-hitzler.de http://www.daselab.org http://www.semantic-web-journal.net http://k-state.edu/ID3A https://neurosymbolic-ai-journal.com