全球能源互联网（英文）2020，Vol.3Issue(2)：186-192,7.DOI:10.14171/j.2096-5117.gei.2020.02.010
基于双向长短时记忆网络和条件随机场的电力实体识别技术研究
Power entity recognition based on bidirectional long short-term memory and conditional random fields
摘要
Abstract
With the application of artificial intelligence technology in the power industry, the knowledge graph is expected to play a key role in power grid dispatch processes, intelligent maintenance, and customer service response provision. Knowledge graphs are usually constructed based on entity recognition. Specifically, based on the mining of entity attributes and relationships, domain knowledge graphs can be constructed through knowledge fusion. In this work, the entities and characteristics of power entity recognition are analyzed, the mechanism of entity recognition is clarified, and entity recognition techniques are analyzed in the context of the power domain. Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated, and the two methods are comparatively analyzed. The results indicated that the CRF model, with an accuracy of 83％, can better identify the power entities compared to the BLSTM. The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.关键词
知识图/实体识别/条件随机域/双向长短期记忆Key words
Knowledge graph/Entity recognition/Conditional Random Fields (CRF)/Bidirectional Long Short-Term Memory (BLSTM)引用本文复制引用
Zhixiang Ji,Xiaohui Wang,Changyu Cai,Hongjian Sun..基于双向长短时记忆网络和条件随机场的电力实体识别技术研究[J].全球能源互联网（英文）,2020,3(2):186-192,7.基金项目
This work was supported by Science and Technology Project of State Grid Corporation(Research and Application of Intelligent Energy Meter Quality Analysis and Evaluation Technology Based on Full Chain Data). （Research and Application of Intelligent Energy Meter Quality Analysis and Evaluation Technology Based on Full Chain Data）
