用大模型+检索增强，让知识库匹配更准更快。
KROMA: Ontology Matching with Knowledge Retrieval and Large Language Models
- 用检索增强生成框架动态补充语义信息
- 在多个基准上超越传统与先进方法
- 适合需要高精度匹配的跨系统集成场景
本研究提出KROMA框架，通过在检索增强生成（RAG）管道中引入大语言模型（LLM），将结构、词汇和定义知识动态融入本体匹配任务的语义上下文。为兼顾性能与效率，KROMA结合双相似度概念匹配与轻量级本体精简步骤，有效削减候选概念数量，显著降低调用LLM带来的通信开销。在多个基准数据集上的实验表明，融合知识检索与上下文增强的LLM可大幅提升匹配效果，优于经典系统与前沿的基于大模型的方法，同时保持通信开销相当。研究验证了目标化知识检索、提示增强及本体精简等优化技术在大规模本体匹配中的可行性与有效性。
原文摘要 · Abstract (English)
Ontology Matching (OM) is a cornerstone task of semantic interoperability, yet existing systems often rely on handcrafted rules or specialized models with limited adaptability. We present KROMA, a novel OM framework that harnesses Large Language Models (LLMs) within a Retrieval-Augmented Generation (RAG) pipeline to dynamically enrich the semantic context of OM tasks with structural, lexical, and definitional knowledge. To optimize both performance and efficiency, KROMA integrates a bisimilarity-based concept matching and a lightweight ontology refinement step, which prune candidate concepts and substantially reduce the communication overhead from invoking LLMs. Through experiments on multiple benchmark datasets, we show that integrating knowledge retrieval with context-augmented LLMs significantly enhances ontology matching, outperforming both classic OM systems and cutting-edge LLM-based approaches while keeping communication overhead comparable. Our study highlights the feasibility and benefit of the proposed optimization techniques (targeted knowledge retrieval, prompt enrichment, and ontology refinement) for ontology matching at scale.
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