Entity Alignment Method for Power Data Knowledge Graph of Semantic and Structural Information

2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation

Zhiqiang, Wang^1 ; Yuan, Wang^2^3 ; Kang, Zhao^2^3 ; Xin, Wang^4 ; Hui, Hu^5

State Grid Zhejiang Information and Telecommunication Co. LTD, Hangzhou, Zhejiang

310020, China^1

NARI Group Corporation, State Grid Electric Power Research Institute, Nanjing, Jiangsu

211000, China^2

China Realtime Database Co.Ltd, Nanjing, Jiangsu

211000, China^3

State Grid Anhui Electric Power Co. LTD, Hefei, Anhui

230061, China^4

Xi'An Jiaotong University, Xi'an, Shanxi

710049, China^5

关键词: Accumulated mass;    Alignment methods;    Business systems;    High quality power;    Information construction;    Knowledge graphs;    Structural information;    Structural models;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/569/5/052103/pdf
DOI  :  10.1088/1757-899X/569/5/052103

来源: IOP

【 摘 要 】

With the continuous deepening of information construction of State Grid Corporation of China, organizing and utilizing the accumulated mass run data effectively and intelligently has become an urgent problem to solve. Knowledge graph has become an increasingly important hot technology for establishing sematic connection network for power data in full-service unified data centre. Entity alignment is one of the key steps for constructing high-quality power knowledge graph to solve the problem of a large number of entity heterogeneity and redundancy existing between different business systems. This paper proposes an entity alignment method for power data with sematic and structural information with a co-training framework. The semantic and structural models are complemented from the other after they are trained under their perspectives separately. The experiment shows the model achieves satisfactory results with higher accuracy and F1.