  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.
