Entity relationship extraction and auxiliary disposal of power equipment fault defect text based on knowledge graph

Xinixn Zhang; Yingtao Sun; Feixiang Yu; Weijian Shen; Minghui Ren

doi:10.1117/12.2645411

23 November 2022 Entity relationship extraction and auxiliary disposal of power equipment fault defect text based on knowledge graph

Xinixn Zhang, Yingtao Sun, Feixiang Yu, Weijian Shen, Minghui Ren

Author Affiliations +

Proceedings Volume 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022); 123020W (2022) https://doi.org/10.1117/12.2645411
Event: Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 2022, Guangzhou, China

Abstract

By using structured, semi-structured, and unstructured data, combined with related algorithms for knowledge extraction, it is one of the current development directions of electric power technology to build a domain knowledge graph. The purpose of this paper is to study entity relation extraction and auxiliary disposal of power equipment fault defect text based on knowledge graph. The construction method of knowledge graph is studied, and the method of constructing a power equipment diagnosis system is used to assist in the disposal of power equipment faults, and the function of the power equipment diagnosis system is analyzed. The system defines 10 entity relationships according to the output of the power equipment fault entity. A method for derivation of power equipment fault entity relationship based on syntactic and semantic features is proposed. Finally, the extraction results of 10 kinds of entity relationships meet the actual use requirements.

Citation Download Citation

Xinixn Zhang, Yingtao Sun, Feixiang Yu, Weijian Shen, and Minghui Ren "Entity relationship extraction and auxiliary disposal of power equipment fault defect text based on knowledge graph", Proc. SPIE 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 123020W (23 November 2022); https://doi.org/10.1117/12.2645411

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