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）是知识整合中的关键任务，通过对齐异构本体实现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，对大型语言模型（LLMs）潜力的探索十分有限。我们提出LLMs4OM框架——一种评估LLMs在OM任务中有效性的新颖方法。该框架包含检索和匹配两个模块，并通过零样本提示在三种本体表示形式（概念、概念-父级、概念-子级）上进行增强。基于来自不同领域的20个OM数据集的全面评估，我们证明：在LLMs4OM框架下，LLMs能够达到甚至超越传统OM系统的性能，尤其在复杂匹配场景中表现突出。我们的研究结果凸显了LLMs对OM领域作出显著贡献的潜力。