计算机工程与应用2024，Vol.60Issue(24)：166-176,11.DOI:10.3778/j.issn.1002-8331.2406-0301
融合大模型微调与图神经网络的知识图谱问答
Combining Large Model Fine-Tuning and Graph Neural Networks for Knowledge Graph Question Answering
摘要
Abstract
To address the challenges posed by inaccurate semantic parsing in traditional knowledge graph question answering systems when processing natural language queries,this paper proposes a method that integrates large model fine-tuning with graph neural networks.The approach begins with the collection of questions and the definition of their corresponding logical forms.Leveraging the robust semantic parsing capabilities of large pre-trained language models,the accuracy of question parsing is significantly enhanced through fine-tuning on question-answer pairs,where each pair includes a ques-tion and its associated logical form.Subsequently,the fuzzy set method is applied to further refine the fine-tuned logical forms,improving retrieval precision.Finally,graph neural networks are employed to perform relation projection and logical operations on these enhanced logical forms to derive the final answers.Experimental validation on standard general-domain datasets,such as WebQSP and ComplexWebQuestions,demonstrates that this method surpasses baseline models in terms of F1,Hit@1,and ACC metrics.Additionally,the method has been successfully applied and validated on domain-specific datasets,including those related to wind power equipment and high-speed trains,confirming its effectiveness in specialized domains.关键词
知识图谱问答/大模型微调/逻辑形式/图神经网络检索Key words
knowledge graph Q&A/large model fine-tuning/logical form/graph neural network retrieval分类
信息技术与安全科学引用本文复制引用
陈俊臻,王淑营,罗浩然..融合大模型微调与图神经网络的知识图谱问答[J].计算机工程与应用,2024,60(24):166-176,11.基金项目
国家重点研发计划项目(2022YFC3005200) （2022YFC3005200）
四川省重大科技专项项目(2022ZDX0003). （2022ZDX0003）
