2024/03/23 by Yihang Zhao, Zhao, Yihang, Neil Vetter +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Semantic Web and Ontologies #Service-Oriented Architecture and Web Services
paper · pdf · doi:10.48550/arxiv.2403.15864
openalex publication_date 2024/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper explores the integration of Large Language Models (LLMs) such as GPT-3.5 and GPT-4 into the ontology refinement process, specifically focusing on the OntoClean methodology. OntoClean, critical for assessing the metaphysical quality of ontologies, involves a two-step process of assigning meta-properties to classes and verifying a set of constraints. Manually conducting the first step proves difficult in practice, due to the need for philosophical expertise and lack of consensus among ontologists. By employing LLMs with two prompting strategies, the study demonstrates that high accuracy in the labelling process can be achieved. The findings suggest the potential for LLMs to enhance ontology refinement, proposing the development of plugin software for ontology tools to facilitate this integration.