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（Ontology Matching, OM）通过对齐相关实体，实现不同本体间的语义互操作性并解决其概念异构性。当前OM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和较新的基于机器学习的预测系统。尽管大语言模型（Large Language Models, LLMs）及其智能体已彻底变革数据工程，并在诸多领域得到创新性应用，但其在OM领域的潜力仍未得到充分探索。本研究为OM系统引入了一种新颖的、由智能体驱动的大语言模型设计范式。针对利用LLM智能体进行OM所面临的若干具体挑战，我们提出了一个通用框架，即Agent-OM（Ontology Matching Agent），该框架包含两个用于检索和匹配的孪生智能体，并配备一组OM工具。我们在一个概念验证系统中实现了该框架。在三个本体对齐评估倡议（Ontology Alignment Evaluation Initiative, OAEI）任务上对现有先进OM系统的评估表明，我们的系统在简单OM任务上能够取得与长期保持的最佳性能非常接近的结果，并且在复杂及少样本OM任务上能显著提升性能。