Ontology enrichment using a large language model: Applying lexical, semantic, and knowledge network-based similarity for concept placement.
Journal:
Journal of biomedical informatics
Published Date:
Jun 19, 2025
Abstract
OBJECTIVE: Ontologies are essential for representing the knowledge of a domain. To make ontologies useful, they must encompass a comprehensive domain view. To achieve ontology enrichment, there is a need to discover new concepts to be added, either because they were missed in the first place, or the state-of-the-art has advanced to develop new real-world concepts. Our goal is to develop an automatic enrichment pipeline using a seed ontology, a Large Language Model (LLM), and source of text. The pipeline is applied to the domain of Social Determinants of Health (SDoH), using PubMed as a source of concepts. In this work, the applicability and effectiveness of the enrichment pipeline is demonstrated by extending the SDoH Ontology called SOHOv1, however our methodology could be used in other domains as well.
