Abstract: Knowledge graph is an important form of knowledge representation,which can integrate and organize information effectively.It has been widely used in search engines,intelligent question answering and recommendation systems.Traditional knowledge graph construction relies on manual annotation and rule-based systems,which is huge in scale and uneven in quality,and is difficult to adapt to the dynamic changes of massive data.Recently,large models have shown superior performance in knowledge generation.However,there is still a lack of research on large language models to enhance knowledge graph error repairing.Therefore,a progressive error correction method for knowledge graphs,assisted by large language models,has been proposed.Using embedding models to evaluate the quality of knowledge triples and high-quality triples as prompts for learning content,knowledge correction by large language models is realized.Based on extensive experiments,the proposed method significantly enhances the reasoning ability of knowledge graphs.

Key words: Knowledge graph, Large language model, Embedding model, Progressive method, Error repair