【摘要】 知识抽取作为构建电力知识图谱的关键步骤，能够从大量非结构化电力文本中准确提取实体和关系。然而，传统的流水线方式存在以下问题：错误信息在识别过程中向后传递，实体识别与关系抽取任务割裂，以及容易产生冗余信息。这些问题导致抽取精确率低、信息不全面，从而影响知识图谱的构建质量。针对这些挑战，提出了一种面向电力信息系统运维领域的实体关系联合抽取模型——EBOM,并对电力信息运维领域常用模型OneRel的目标函数进行了优化，以提升其在电力知识三元组抽取中的精确率。基于电力信息系统运行监控数据和故障文本数据进行实验，构建了电力信息系统运维领域的知识图谱。结果表明，EBOM模型相较于多模块多步骤模型PRGC,在知识抽取精确率上提升了约8个百分点，为电力信息运维领域知识图谱的构建提供了有效支持。更多还原

【Abstract】 Knowledge extraction serves as a critical step in constructing power knowledge graphs, enabling the accurate extraction of entities and relations from a large volume of unstructured power-related texts. However, traditional pipeline methods face several issues: Error propagation during the recognition process, the decoupling of entity recognition and relation extraction tasks, and the generation of redundant information. These problems lead to low extraction precision and incomplete information, thereby affecting the quality of knowledge graph construction. To address these challenges, an entity-relation joint extraction method tailored for the power information system operation and maintenance domain, named Ele-RoBERTa-OneRel model(EBOM), is proposed. The score function of the OneRel model for this domain is optimized to improve its precision in extracting power knowledge triplets. Experiments using monitoring data and fault text data from power information systems result in the construction of a knowledge graph for the power information system operation and maintenance domain. Results indicate that the EBOM improves extraction precision by 8 percentage points compared to the multi-module, multi-step PRGC model, providing effective support for the construction of knowledge graphs in the power information system operation and maintenance domain.更多还原