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.

本研究解决了知识图谱匹配中的表达能力不足问题，提出了一种将大型语言模型（LLMs）整合到本体匹配中的新方法，通过对实例子图的相似环境进行匹配生成表达性对应关系。研究表明，这种整合显著提升了模型性能，实现了F-measure提高45%的显著成果。

复杂本体匹配与大型语言模型嵌入