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, 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 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（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质性。当前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与新兴的基于机器学习的预测系统。尽管大型语言模型（LLM）及LLM智能体已彻底革新数据工程领域，并在众多领域展现出创造性应用，但其在本体匹配中的潜力尚未得到充分探索。本研究提出一种基于LLM智能体的新型OM系统设计范式。针对LLM智能体应用于OM所面临的若干具体挑战，我们构建了一个通用框架Agent-OM，该框架包含两个用于检索与匹配的孪生智能体，并配备一组基于简单提示的OM工具。我们通过概念验证系统实现了该框架。在三个本体对齐评估倡议（OAEI）数据集上，与现有最优OM系统的对比评估表明：我们的系统在简单OM任务上可取得与长期最佳性能极为接近的结果，并在复杂及少样本OM任务中显著提升性能。