Construction and application of network security knowledge graph for power monitoring system

Abstract

Facing the network security problem of power monitoring system, this paper studies the knowledge graph-based network security knowledge extraction and management method. In the entity recognition module, an entity recognition algorithm based on the idea of multi task learning is adopted, which decomposes the entity recognition process into entity boundary perception and entity classification to reduce the impact of error accumulation. Meanwhile, the introduction of comprehensive description information of entity categories into entity classification tasks can not only be widely used in general fields, but also be targeted to specific scenes in the field of power monitoring system. In the relation extraction module, based on the idea of multi-instance learning, the method of piecewise convolution neural network is employed, which reduces the impact of some instance annotation errors. The relation extraction method can extract text feature expression and inter entity structure information, which has a good effect on specialized field knowledge. Finally, based on the established knowledge graph, this paper studies the application problems of network security knowledge management and intelligent question answering of power monitoring system, which shows the effectiveness of knowledge graph technology in the field of network security management.