Question answering system based on the combination of large language model and knowledge graph

In recent years, large language models (LLMs) have achieved remarkable progress in natural language processing. However, their application in question-answering systems continues to face challenges such as insufficient credibility and interpretability of responses, as well as high computational resource demands. To address these issues, this paper proposes a question-answering system that integrates knowledge graphs with lightweight LLMs. Specifically, a lightweight front-end model based on BERT and T5 is employed to extract and transform logical forms from natural language queries, which are then executed on a knowledge graph. Subsequently, a smaller-scale LLM generates credible and interpretable answers based on these query results. Experimental results show that the proposed method achieves F1 scores of 75.6% and 76.8% on the WebQSP and GRAILQA datasets, respectively, surpassing other representative approaches. Furthermore, integrating the extracted knowledge with the ChatGLM-6B model significantly improves answer quality, increasing ratings by 112.8% for simple questions and 77.4% for complex questions, thus validating the effectiveness of our approach.