高电压技术2024，Vol.50Issue(11)：4912-4922,中插10-中插11,13.DOI:10.13336/j.1003-6520.hve.20230772
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
摘要
Abstract
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 back-ward 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 PowerRob-ertsa 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 dis-tribution 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Key words
natural language processing/power knowledge graph/knowledge extraction/joint extraction of entity rela-tions/sequence labeling/relationship overlap引用本文复制引用
束嘉伟,杨挺,耿毅男,于洁..面向电力知识图谱构建的重叠实体关系联合抽取方法[J].高电压技术,2024,50(11):4912-4922,中插10-中插11,13.基金项目
国家重点研发计划(2022YFB2403800) （2022YFB2403800）
国家自然科学基金(U2066213) （U2066213）
天津市自然科学基金重点项目(21JCZDJC00640).Project supported by National Key R&D Program of China(2022YFB2403800),National Natural Science Foundation of China(U2066213),Natural Science Foundation Key Project of Tianjin(21JCZDJC00640). （21JCZDJC00640）
