LLMs4OM: Matching Ontologies with Large Language Models
arxiv(2024)
摘要
Ontology Matching (OM), is a critical task in knowledge integration, where
aligning heterogeneous ontologies facilitates data interoperability and
knowledge sharing. Traditional OM systems often rely on expert knowledge or
predictive models, with limited exploration of the potential of Large Language
Models (LLMs). We present the LLMs4OM framework, a novel approach to evaluate
the effectiveness of LLMs in OM tasks. This framework utilizes two modules for
retrieval and matching, respectively, enhanced by zero-shot prompting across
three ontology representations: concept, concept-parent, and concept-children.
Through comprehensive evaluations using 20 OM datasets from various domains, we
demonstrate that LLMs, under the LLMs4OM framework, can match and even surpass
the performance of traditional OM systems, particularly in complex matching
scenarios. Our results highlight the potential of LLMs to significantly
contribute to the field of OM.
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