An interpretable logic KBQA method based on open-source large language models

Knowledge Base Question Answering (KBQA) aims to find correct answers to natural language questions by reasoning over large-scale knowledge bases.The main challenge is multi-hop reasoning, which requires inferring answers through multiple edges and nodes.Due to high data annotation costs, most datasets only provide final answer annotations, leaving the reasoning process unknown.The model cannot provide the reasoning path along with the answer, making the answer uninterpretable.To address this, we propose a generation-retrieval multi-hop KBQA method combining large language models (LLMs) and the logic programming language Prolog.In the generation phase, we fine-tune the open-source LLMs to generate the logical form of the corresponding prolog representation for the natural language question.In the retrieval phase, the model uses prolog query to reasoning over the KB to get the final answer and the reasoning path.A transparent reasoning path also helps identify and correct errors, enhancing the model's reliability and practicality.Experimental results on two standard KBQA datasets, MetaQA and WebQSP, demonstrate that our method outperforms existing models in both performance and interpretability.