Large Language Model Driven Multi-relational Knowledge Graph Completion Method
Knowledge graphs transform complex Internet information into an easily understandable structured format,significant-ly enhancing the accessibility of information.Knowledge graph completion(KGC)techniques further enhance the completeness of knowledge graphs,markedly improved the performance and user experience of general domain applications such as intelligent question answering and recommendation systems by enhancing the information completeness of knowledge graphs.However,most existing KGC methods focus on triple instances in scenarios with fewer types of relationships and simpler semantics,failing to fully leverage the potential of graphs in handling multi-relational and complex semantics.In response to this issue,we propose a method for multi-relational knowledge graph completion driven by large language model(LLM).By combining the deep linguis-tic understanding capabilities of LLM with the structural characteristics of knowledge graphs,this method effectively captures complex semantic scenarios and comprehends multi-relational relationships.Additionally,we introduce a chain-of-thought based prompting engineering strategy,aiming to enhancing the accuracy of the completion tasks.Experimental results on two public knowledge graph datasets have demonstrated the significant performance improvements of this method.
Knowledge graphLarge language modelKnowledge graph completionMulti-relationalCandidate set constructionChain-of-thought prompt
万方数据
维普
