这篇论文用多个LLM智能体合作搞定本体匹配，能同时发现等价和包含关系，在四个数据集上比传统方法强。
传统本体匹配（OM）仅处理等价或包含关系之一。本文提出混合本体匹配（HOM）任务，并设计基于LLM的多智能体框架AgentMap。AgentMap通过语义检索、层次搜索和协作推理，在源本体概念中寻找等价概念或最细粒度包含者。在扩展的四个OM数据集混合设置下，AgentMap取得良好性能，同时在等价和包含单独设置中分别超过基线。
AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching
Ontology matching (OM) has traditionally been formulated as either equivalence discovery or subsumption matching. The existing OM systems identify only one type of semantic correspondence and cannot simultaneously discover equivalence and subsumption mappings. In this paper, we introduce Hybrid Ontology Matching (HOM), a new OM task that unifies equivalence and subsumption discovery, and accordingly propose a Large Language Model (LLM)-based multi-agent OM framework AgentMap that is implemented by a series of interdependent semantic decisions. Given a concept in the source ontology, AgentMap integrates semantic retrieval, hierarchical search, and collaborative multi-agent LLM reasoning to progressively explore the target ontology, identifying either the equivalent concept, if one exists, or the most fine-grained subsumer. We further extend four OM datasets for a HOM benchmark and evaluate AgentMap under hybrid, equivalence-only, and subsumption-only settings. Experimental results show that AgentMap achieves promising performance on the hybrid setting, and at the same time outperforms equivalence matching and subsumption matching baselines on the equivalence-only and subsumption-only settings, respectively.