现代电子技术2025，Vol.48Issue(2)：137-142,6.DOI:10.16652/j.issn.1004-373x.2025.02.022
面向区块链漏洞知识库的大模型增强知识图谱问答模型
Large model enhanced knowledge graph question answering model for blockchain vulnerability knowledge base
摘要
Abstract
There are limitations in the application of large language models(LLMs)in professional fields,especially in the field of blockchain vulnerabilities,such as noise interference of technical terms and insufficient understanding caused by excessive fine-grained information.On this basis,an enhanced knowledge graph question answering model for blockchain vulnerability knowledge base(LMBK_KG)is constructed,which can enhance the knowledge representation and comprehension ability by integrating large models and knowledge graphs,and filter and accurately match professional problems by means of multi-granularity semantic information.The research methods include using integrated multi-granularity semantic information and knowledge graph to filter the professional term noise,and using large model-generated answers for structured matching and validation with the professional knowledge graph to improve the robustness and security of the model.The experimental results show that,in comparison with the large model used alone,the proposed model can improve the accuracy of question answering in the field of blockchain vulnerabilities by 26%.关键词
大语言模型/知识图谱/问答模型/多粒度语义信息/区块链/漏洞信息/文本表征Key words
large language model/knowledge graph/question-answering model/multi-granularity semantic information/blockchain/vulnerability information/text representation分类
电子信息工程引用本文复制引用
解飞,宋建华,姜丽,张龑,何帅..面向区块链漏洞知识库的大模型增强知识图谱问答模型[J].现代电子技术,2025,48(2):137-142,6.基金项目
国家自然科学基金项目(62377009) （62377009）
湖北省重点研发计划重点项目(2021BAA188) （2021BAA188）
湖北省重点研发计划重点项目(2021BAA184) （2021BAA184）
湖北省高等学校人文社会科学重点研究基地绩效评价信息管理研究中心课题(2020JX01) （2020JX01）
湖北省科技计划重大科技专项(2024BAA008) （2024BAA008）
