On first-order logic reasoning with small language models, the compact CLIF grammar achieves accuracy close to natural language, though natural language remains the best representation.
Using Large Language Models for OntoClean-based Ontology Refinement
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
citation-role summary
background 1
citation-polarity summary
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Investigating Language Model Capabilities to Represent and Process Formal Knowledge: A Preliminary Study to Assist Ontology Engineering
On first-order logic reasoning with small language models, the compact CLIF grammar achieves accuracy close to natural language, though natural language remains the best representation.