Ontology matching (OM) enables semantic interoperability between different ontologies and resolves their conceptual heterogeneity by aligning related entities. OM systems currently have two prevailing design paradigms: conventional knowledge-based expert systems and newer machine learning-based predictive systems. While large language models (LLMs) and LLM agents have revolutionised data engineering and have been applied creatively in many domains, their potential for OM remains underexplored. This study introduces a novel agent-powered LLM-based design paradigm for OM systems. With consideration of several specific challenges in leveraging LLM agents for OM, we propose a generic framework, namely Agent-OM (Agent for Ontology Matching), consisting of two Siamese agents for retrieval and matching, with a set of OM tools. Our framework is implemented in a proof-of-concept system. Evaluations of three Ontology Alignment Evaluation Initiative (OAEI) tracks over state-of-the-art OM systems show that our system can achieve results very close to the long-standing best performance on simple OM tasks and can significantly improve the performance on complex and few-shot OM tasks.

翻译：本体匹配通过对齐相关实体，实现不同本体间的语义互操作，并解决其概念异构性。当前本体匹配系统主要有两种主流设计范式：传统的基于知识的专家系统与较新的基于机器学习的预测系统。尽管大语言模型及其智能体已彻底变革数据工程领域，并在诸多领域得到创新性应用，但其在本体匹配中的潜力仍未得到充分探索。本研究引入了一种新颖的、由智能体驱动的大语言模型本体匹配系统设计范式。针对利用大语言模型智能体进行本体匹配所面临的若干具体挑战，我们提出了一个通用框架——Agent-OM（用于本体匹配的智能体），该框架由两个用于检索与匹配的孪生智能体及一组本体匹配工具构成。我们通过一个概念验证系统实现了该框架。在三个本体对齐评估倡议赛道上的评估表明，与当前最先进的本体匹配系统相比，我们的系统在简单本体匹配任务上能够取得与长期保持的最佳性能极为接近的结果，并在复杂与小样本本体匹配任务上能显著提升性能。