RoUIE: A Method for Constructing Knowledge Graph of Power Equipment Based on Improved Universal Information Extraction

Zhenhao Ye, Donglian Qi (), Hanlin Liu, Yunfeng Yan, Qihao Chen and Xiayu Liu
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Zhenhao Ye: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Donglian Qi: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Hanlin Liu: School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
Yunfeng Yan: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Qihao Chen: Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China
Xiayu Liu: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2024, vol. 17, issue 10, 1-18

Abstract: The current state evaluation of power equipment often focuses solely on changes in electrical quantities while neglecting basic equipment information as well as textual information such as system alerts, operation records, and defect records. Constructing a device-centric knowledge graph by extracting information from multiple sources related to power equipment is a valuable approach to enhance the intelligence level of asset management. Through the collection of pertinent authentic datasets, we have established a dataset for the state evaluation of power equipment, encompassing 35 types of relationships. To better suit the characteristics of concentrated relationship representations and varying lengths in textual descriptions, we propose a generative model called RoUIE, which is a method for constructing a knowledge graph of power equipment based on improved Universal Information Extraction (UIE). This model first utilizes a pre-trained language model based on rotational position encoding as the text encoder in the fine-tuning stage. Subsequently, we innovatively leverage the Distribution Focal Loss (DFL) to replace Binary Cross-Entropy Loss (BCE) as the loss function, further enhancing the model’s extraction performance. The experimental results demonstrate that compared to the UIE model and mainstream joint extraction benchmark models, RoUIE exhibits superior performance on the dataset we constructed. On a general Chinese dataset, the proposed model also outperforms baseline models, showcasing the model’s universal applicability.

Keywords: state evaluation; power equipment; universal information extraction; knowledge graph construction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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