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This work investigates the applicability of recent generative Large LanguageModels (LLMs), such as the GPT series and Flan-T5, to ontology alignment foridentifying concept equivalence mappings across ontologies. To test thezero-shot performance of Flan-T5-XXL and GPT-3.5-turbo, we leverage challengingsubsets from two equivalence matching datasets of the OAEI Bio-ML track, takinginto account concept labels and structural contexts. Preliminary findingssuggest that LLMs have the potential to outperform existing ontology alignmentsystems like BERTMap, given careful framework and prompt design.

Exploring Large Language Models for Ontology Alignment