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- W4387011704 abstract "The electric power energy industry, after decades of development, already possesses a large amount of technical literature in Chinese, which contains a wealth of expert knowledge. Intelligent decision-making would undoubtedly be faster and more accurate if knowledge could be accurately extracted and presented to employees in an understandable form or in an intelligent QA system. Facing the impact of massive electric text data, the technical problem addressed in this paper is to propose an end-to-end model based on graph convolutional neural networks and a multi-headed attention mechanism that combines contextual semantic features and syntactic features of text sequences to effectively improve the performance of the knowledge extraction task. Researching knowledge organisation methods for heterogeneous data in distribution network operation and maintenance, constructing expert knowledge-based extraction models and realising knowledge representation that flexibly and clearly expresses business logic can help improve the automation of power systems and provide global knowledge support for smart grids." @default.
- W4387011704 created "2023-09-26" @default.
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- W4387011704 date "2023-09-25" @default.
- W4387011704 modified "2023-09-26" @default.
- W4387011704 title "An end-to-end power knowledge extraction method based on convolutional neural networks and multi-headed attention mechanisms" @default.
- W4387011704 doi "https://doi.org/10.1117/12.3005282" @default.
- W4387011704 hasPublicationYear "2023" @default.
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