面向电力知识图谱构建的重叠实体关系联合抽取方法  

Joint Extraction Method for Overlapping Entity Relationships in the Construction of Electric Power Knowledge Graph

作　　者：束嘉伟 杨挺 耿毅男 于洁[1] SHU Jiawei;YANG Ting;GENG Yinan;YU Jie(Smart Grid Key Laboratory of the Ministry of Education(Tianjin University),Tianjin 300072,China)

机构地区：[1]智能电网教育部重点实验室(天津大学),天津300072

出　　处：《高电压技术》2024年第11期4912-4922,I0010,I0011,共13页High Voltage Engineering

基　　金：国家重点研发计划(2022YFB2403800);国家自然科学基金(U2066213);天津市自然科学基金重点项目(21JCZDJC00640)。

摘　　要：作为构建电力知识图谱的关键步骤,知识抽取可以从海量非结构化电力文本中准确抽取出实体和关系。但是,传统流水线式方法存在识别的错误信息后向传递、实体识别和关系抽取任务割裂以及易产生冗余信息的问题,进而导致抽取准确率低、抽取信息不全面,最终影响知识图谱的准确构建。针对上述问题,提出面向电力知识图谱构建的重叠实体关系联合抽取方法,通过改进的序列标注方案进行联合抽取,构建了电力领域专属预训练PowerRoberta模型,并增加对抗训练,提高了模型抽取电力知识的准确度和对陌生信息的预测能力。最后,以实际变电站巡检数据为例进行了实验分析与配电知识图谱可视化构建,结果表明所提出的联合抽取方法提升了知识抽取的准确率,准确率达到91.67%,可有效支撑配电网智能信息检索、辅助决策高级应用。As a key step in building a power knowledge graph,knowledge extraction can accurately extract entities and relationships from massive unstructured power texts.However,the traditional pipeline method has the problems of backward transmission of error information,separation of entity recognition,and relationship extraction tasks,and is easy to generate redundant information,which results in low extraction accuracy,incomplete extraction of information,and ulti-mately impairs the accurate construction of the knowledge graph.To solve the above problems,this paper proposes a joint extraction method of overlapping entity relationships for the construction of the power knowledge graph.Through the improved sequence labeling scheme,the joint extraction is carried out,the exclusive pre-training model(the PowerRobertsa model)in the power field is constructed,and the confrontation training is increased,which improves the accuracy of the model extraction of power knowledge and the ability to predict unfamiliar information.Finally,by taking the actual substation patrol data as an example,the experimental analysis and the visual construction of the distribution Knowledge graph are carried out.The results show that the joint extraction method proposed in this paper can be adopted to improve the accuracy of knowledge extraction,which reaches 91.67%,and can effectively support the advanced application of distribution network intelligent information retrieval and decision-making assistance.

关 键 词：自然语言处理 电力知识图谱 知识抽取 实体关系联合抽取 序列标注 关系重叠 

分 类 号：F407.61[经济管理—产业经济] TP391.1[自动化与计算机技术—计算机应用技术]