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-based agents have become revolutionary in data engineering and have been applied creatively in various domains, their potential for OM remains underexplored. This study introduces a novel agent-powered LLM-based design paradigm for OM systems. With thoughtful consideration of several specific challenges to leverage LLMs for OM, we propose a generic framework, namely Agent-OM, consisting of two Siamese agents for retrieval and matching, with a set of simple prompt-based 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 very close results to the best long-standing performance on simple OM tasks and significantly improve the performance on complex and few-shot OM tasks.

翻译：本体匹配（OM）通过对齐相关实体，实现不同本体之间的语义互操作性，解决其概念异构性问题。目前OM系统存在两种主流设计范式：传统的基于知识的专家系统，以及较新的基于机器学习的预测系统。尽管大型语言模型（LLM）和基于LLM的智能体已在数据工程领域引发革命性变革，并在多个领域得到创造性应用，但它们在OM领域的潜力尚未被充分探索。本研究提出了一种基于LLM的新型智能体驱动设计范式，用于构建OM系统。在审慎考量利用LLM进行本体匹配所需应对的若干特定挑战后，我们提出一个通用框架——Agent-OM，该框架由两个用于检索与匹配的孪生智能体组成，并配备一组基于简单提示的OM工具。该框架已在一个概念验证系统中实现。在三个本体对齐评估倡议（OAEI）评测集上，与当前最先进的OM系统相比，我们的系统在简单OM任务上能够取得与长期最佳表现非常接近的结果，并在复杂及少样本OM任务上显著提升性能。