Exploring Large Language Models for Ontology Alignment

Yuan He; Jiaoyan Chen; Hang Dong; Ian Horrocks

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arXiv · 2309.07172

Exploring Large Language Models for Ontology Alignment

Yuan He·Jiaoyan Chen·Hang Dong·Ian Horrocks

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Abstract

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.

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Yuan He, Jiaoyan Chen, Hang Dong, Ian Horrocks. 2023-09-12. Exploring Large Language Models for Ontology Alignment. https://arxiv.org/abs/2309.07172

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