GenOM: Ontology Matching with Description Generation and Large Language Model
This addresses semantic interoperability for biomedical knowledge integration, though it appears incremental as it builds on existing LLM-based methods with specific enhancements.
The paper tackles ontology matching in biomedical domains by introducing GenOM, an LLM-based framework that generates textual definitions to enrich semantic representations and incorporates exact matching tools, achieving competitive performance on the OAEI Bio-ML track and surpassing many baselines.
Ontology matching (OM) plays an essential role in enabling semantic interoperability and integration across heterogeneous knowledge sources, particularly in the biomedical domain which contains numerous complex concepts related to diseases and pharmaceuticals. This paper introduces GenOM, a large language model (LLM)-based ontology alignment framework, which enriches the semantic representations of ontology concepts via generating textual definitions, retrieves alignment candidates with an embedding model, and incorporates exact matching-based tools to improve precision. Extensive experiments conducted on the OAEI Bio-ML track demonstrate that GenOM can often achieve competitive performance, surpassing many baselines including traditional OM systems and recent LLM-based methods. Further ablation studies confirm the effectiveness of semantic enrichment and few-shot prompting, highlighting the framework's robustness and adaptability.