Enhancing Ontologies with Large Language Models: A Semi-Automated Approach

Abstract

The process of creating and maintaining domain ontologies is a time- and resource-intensive activity, given the dynamic nature of domain knowledge and the regular introduction of new terms. This study aims to determine the effectiveness of large language models (LLMs) in augmenting the domain ontology authoring process. We fine-tuned state-of-the-art pre-trained LLMs and evaluated their performance on two tasks: synonym identification and parent-child relationship identification. The models achieved 98% accuracy in the first task and 75.4% accuracy in the second, demonstrating significant capabilities in automating synonym identification and relationship classification. In addition to providing a methodological basis for further extending and improving these results, we demonstrate that LLMs can be effectively used in ontology development and maintenance. This can save time and effort in the process.