用大模型提升知识图谱匹配的表达能力，效果显著。
Complex Ontology Matching with Large Language Model Embeddings
- 通过实例子图环境匹配生成语义丰富的对应关系
- 引入大模型后F值相比基线提升45%
- 适合需要高精度知识对齐的研究者使用
知识图谱匹配是一项具有挑战性的任务，其表达能力尚未充分挖掘。尽管嵌入与语言模型在此任务中应用日益广泛，但现有方法在生成富有表现力的对应关系时仍未充分发挥大语言模型（LLMs）的潜力。本文提出将大语言模型融入基于对齐需求和基于ABox的关系发现框架中，通过匹配实例子图的相似环境来生成对应关系。该方法在标签相似性、子图匹配和实体匹配三个层面进行了架构改进。对比了词嵌入、句嵌入及基于大模型的嵌入性能，结果表明，整合大模型的方法优于所有其他模型，在基线基础上实现F-measure提升45%。
原文摘要 · Abstract (English)
Ontology, and more broadly, Knowledge Graph Matching is a challenging task in which expressiveness has not been fully addressed. Despite the increasing use of embeddings and language models for this task, approaches for generating expressive correspondences still do not take full advantage of these models, in particular, large language models (LLMs). This paper proposes to integrate LLMs into an approach for generating expressive correspondences based on alignment need and ABox-based relation discovery. The generation of correspondences is performed by matching similar surroundings of instance sub-graphs. The integration of LLMs results in different architectural modifications, including label similarity, sub-graph matching, and entity matching. The performance word embeddings, sentence embeddings, and LLM-based embeddings, was compared. The results demonstrate that integrating LLMs surpasses all other models, enhancing the baseline version of the approach with a 45\% increase in F-measure.
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