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. 

Key words: knowledge extraction, entity-relation joint extraction, power information system