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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年10月4日

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  • Jihong Wang,
  • Yichen Zhang,
  • Wei Liu

摘要

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.