Research on knowledge extraction and linking methods for electric power policy documents based on knowledge graph

Gang Sun; Ruoyun Hu; Qingjuan Wang; Wanjing Song

doi:10.1117/12.3026830

27 March 2024 Research on knowledge extraction and linking methods for electric power policy documents based on knowledge graph

Gang Sun, Ruoyun Hu, Qingjuan Wang, Wanjing Song

Author Affiliations +

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131052G (2024) https://doi.org/10.1117/12.3026830
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In the era of big data, power companies have accumulated a large number of policy documents at the government, industry, and company levels. Structuring these policy documents, efficiently extracting their core content, and fully exploiting their value is of great significance for optimizing business models, supporting management decisions, and building digital services in power companies. With the development of artificial intelligence technology, knowledge graphs have emerged. Knowledge graphs can link fragmented knowledge through relationships, building structured document knowledge bases, making the knowledge contained in the documents easier to understand, query, manage, and apply.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gang Sun, Ruoyun Hu, Qingjuan Wang, and Wanjing Song "Research on knowledge extraction and linking methods for electric power policy documents based on knowledge graph", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131052G (27 March 2024); https://doi.org/10.1117/12.3026830

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