Large model enhanced knowledge graph question answering model for blockchain vulnerability knowledge base
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%.
large language modelknowledge graphquestion-answering modelmulti-granularity semantic informationblockchainvulnerability informationtext representation
万方数据
维普
