Exploring Large Language Models for Ontology Alignment
Yuan He Jiaoyan Chen Hang Dong Ian Horrocks
This work investigates the applicability of recent generative Large Language
Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for
identifying concept equivalence mappings across ontologies. To test the
zero-shot performance of Flan-T5-XXL and GPT-3.5-turbo, we leverage challenging
subsets from two equivalence matching datasets of the OAEI Bio-ML track, taking
into account concept labels and structural contexts. Preliminary findings
suggest that LLMs have the potential to outperform existing ontology alignment
systems like BERTMap, given careful framework and prompt design.