Construction of Knowledge Graph of Power Communication Planning based on Deep Learning

Abstract

In order to improve the intelligent level of power communication network planning, a method for constructing a knowledge graph of power communication planning based on deep learning is proposed for the problems of lengthy power communication planning report text and low information extraction efficiency. This article takes the power communication planning text as the research object, constructs the knowledge organization structure of the power communication knowledge graph from top to bottom, and defines the entity concept and the relation concept. A variety of deep learning models are comprehensively used for knowledge extraction. Bi-LSTM-CRF model is used for named entity recognition, and PCNN model is used for entity relationship extraction, forming entity relationship table in power communication planning text. The effectiveness of the above method is verified by simulation experiment. Finally, the data storage and visualization are realized through Neo4j graph database.