Combining Large Model Fine-Tuning and Graph Neural Networks for Knowledge Graph Question Answering

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 question 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. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.