AgentMap：用于本体匹配的联合等价与包含关系发现
AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching
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中文总结 AI 辅助
本文提出统一等价与包含发现的混合本体匹配任务，构建基于LLM的多智能体框架AgentMap，在三类设置下的本体匹配任务中均取得优异性能。
中文摘要 AI 辅助
本体匹配（OM）传统上被表述为等价关系发现或包含关系匹配，现有OM系统仅能识别一种语义对应类型，无法同时发现等价和包含映射。本文提出统一等价与包含发现的新型OM任务——混合本体匹配（HOM），并据此构建基于大语言模型（LLM）的多智能体OM框架AgentMap，其由一系列相互关联的语义决策实现。给定源本体中的一个概念，AgentMap整合语义检索、分层搜索及协作多智能体LLM推理，逐步探索目标本体，若存在等价概念则识别该概念，否则识别最细粒度的上位概念。本文进一步扩展四个OM数据集以构建HOM基准，并在混合、仅等价、仅包含三种设置下评估AgentMap。实验结果显示，AgentMap在混合设置下表现优异，同时在仅等价和仅包含设置下分别优于等价匹配和包含匹配的基线方法。
英文摘要
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.
发表机构
- The University of Manchester（曼彻斯特大学）
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
