Abstract—As one of the core technologies of general artificial intelligence, knowledge graph reasoning aims to infer new knowledge from existing knowledge in the knowledge base, providing decision support for knowledge-driven intelligent information services such as information retrieval, question answering, and recommendation systems. However, there are still some issues, such as poor interpretability and low reasoning efficiency, always decrease the current knowledge reasoning performance. To tackle the challenges, this paper proposes a knowledge graph reasoning method LLM4QA, which leverages fine-tuned large language models with chain-of-thought to generate graph query languages SPARQL (i.e., SPARQL Protocol and RDF Query Language) for reasoning. Firstly, an efficient instruction fine-tuning method is applied to fine-tune open-source large language models with chain-of-thought. Then, the fine-tuned open-source large model is used to convert natural language questions into logical forms. Finally, we utilize unsupervised entity relationship retrieval to generate graph database query languages, real-izing a natural language knowledge graph question-answering framework. Experimental results demonstrate that this method achieves well performance in terms of inference accuracy and significantly improves model retrieval efficiency.
 
Keywords—Large Language Model (LLM), knowledge graph, question answering system, chain of thought


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