基于Bert_TextCNN模型的漏洞知识库问答系统

Vulnerability knowledge base Q&A system based on Bert_TextCNN model

融合知识图谱(knowledge graph)在特定领域做问答系统是当下热点之一,用户的意图识别任务是问答系统中至关重要的一步.针对不同漏洞平台管理漏洞情报存在差异、内容不全面和信息孤立,以及用户获取全面的漏洞情报困难等问题,提出构建融合CNVD和CNNVD的综合漏洞知识图谱,在此基础上构建基于Bert_TextCNN意图识别的漏洞知识库问答系统.对比5个主流模型的准确率、召回率和F1值,结果显示Bert_TextCNN模型的F1值可达96.5%,比对照组中最高的F1值高2.4%,说明在意图识别任务中Bert_TextCNN模型的意图识别能力优于其他模型.

Q&A system based on Fusion Knowledge Graph in a specific domain is one of the hot spots nowadays,and the user’s intention identification task is a crucial step in the Q&A system.In response to the problems of differences in vulnerability intelligence managed by different vulnerability platforms,incomplete content and isolated information,and difficulties for users to obtain comprehensive vulnerability intelligence,we proposed to build a comprehensive vulnerability knowledge graph fused with CNVD and CNNVD,and on this basis,we built a vulnerability knowledge base Q&A system based on Bert_TextCNN intent recognition.Comparing the accuracy,recall and F1 value of five mainstream text classification models,the comparison results showed that the F1 value of Bert_TextCNN model could reach 96.5%,which was 2.4%higher than the highest F1 value of the control groups,indicating that the text classification ability of Bert_TextCNN model was better than other models in the intention recognition task.