Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 100-108.doi: 10.11896/jsjkx.210900018

• Intelligent Computing • Previous Articles     Next Articles

Fast and Transmissible Domain Knowledge Graph Construction Method

DENG Kai, YANG Pin, LI Yi-zhou, YANG Xing, ZENG Fan-rui, ZHANG Zhen-yu   

  1. Schoolof Cyber Science and Engineering,Sichuan University,Chengdu 610207,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:DENG Kai,born in 1996,postgraduate.His main research interests include knowledge graph and network application security.
    YANG Pin,born in 1967,Ph.D,professor.His main research interests include software security and network security.

Abstract: Domain knowledge graph can clearly and visually represent domain entity relations,acquire knowledge efficiently and accurately.The construction of domain knowledge graph is helpful to promote the development of information technology in rela-ted fields,but the construction of domain knowledge graph requires huge manpower and time costs of experts,and it is difficult to migrate to other fields.In order to reduce the manpower cost and improve the versatility of knowledge graph construction me-thod,this paper proposes a general construction method of domain knowledge graph,which does not rely on a large of artificial ontology construction and data markup.The domain knowledge graph is constructed through four steps:domain dictionary construction,data acquisition and cleaning,entity linking and maintenance,and graph updating and visualization.This paper takes the domain of network security as an example to construct the knowledge graph and details the build process.At the same time,in order to improve the domain correlation of entities in the knowledge graph,a fusion model based on BERT(Bidirectional Encoder Representations from Transformers) and attention mechanism model is proposed in this paper.The F-score of this model in text classification is 87.14%,and the accuracy is 93.51%.

Key words: Entity classification, Knowledge graph construction, Network security, Text classification

CLC Number: 

  • TP391
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