Ontology Coverage Analysis through Language Models

Ontologies play a crucial role in supporting data and knowledge management in industry by promoting data standardization, interoperability, and facilitating knowledge sharing. However, the growing number of ontologies available in the repositories has made it challenging for developers to select the appropriate ontology for ensuring data interoperability in specific domains. To address this, we propose a method based on artificial intelligence to evaluate how well an ontology covers a particular domain. The results demonstrated that our method effectively identified the most appropriate ontology for each domain. The incorporation of language models in the method enabled it to overcome the limitations of traditional approaches, which often depend on exact string matches. Our method has proven to be an effective tool for assessing how well ontologies cover specific domains, thereby supporting the identification and selection of the most suitable ontologies for intelligent engineering applications.