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.

翻译：本体匹配（Ontology Matching，OM）是知识整合中的一项关键任务，通过对齐异构本体促进数据互操作性和知识共享。传统OM系统通常依赖专家知识或预测模型，但对于大语言模型（LLMs）在该领域的潜力探索有限。我们提出LLMs4OM框架，这是一种评估LLMs在OM任务中有效性的创新方法。该框架分别利用检索和匹配两个模块，通过跨三种本体表示（概念、概念-父类、概念-子类）的零样本提示增强性能。基于20个跨领域OM数据集的综合评估表明，在LLMs4OM框架下，LLMs能够匹配甚至超越传统OM系统的性能，尤其在复杂匹配场景中。我们的结果突显了LLMs为OM领域做出重要贡献的巨大潜力。