广东工业大学学报2025，Vol.42Issue(5)：77-85,9.DOI:10.12052/gdutxb.240139
面向变压器缺陷知识图谱构建的ET-FSUIE关系抽取模型
Research on the ET-FSUIE Relation Extraction Model for Transformer Defect Graph Construction
摘要
Abstract
In the context of digital operation and maintenance of power grids,the surge in unstructured transformer data has made defect information extraction and fault tracing challenging,hindering the transition to intelligent maintenance.Knowledge graph technology offers a potential solution by leveraging its structured nature to integrate operational data and improve efficiency.Inspired by this,this paper proposes a unified short-text format for electrical equipment defects and constructs a high-quality transformer defect relationship dataset.The ET-FSUIE(Electrical Transformer-Fuzzy Span Universal Information Extraction)model is introduced,which integrates a 20%pruned Roformer v2 pre-trained language model.By utilizing its rotary position encoding,the model effectively handles variations in defect description text lengths,enhancing text comprehension.Additionally,a W-FSL(Wasserstein-Fuzzy Span Loss)loss function based on Wasserstein distance is proposed to overcome the limitations of traditional loss functions and improve extraction accuracy.Experimental results on both public and self-built datasets demonstrate the superior performance of the ET-FSUIE model,achieving F1 scores of 81.84%and 88.67%.Finally,a knowledge graph for power transformer defect relationships is constructed using the extracted triplets,providing robust support for the intelligent transformation of power equipment operation and maintenance.关键词
变压器缺陷/知识图谱/生成式语言模型/关系抽取/通用信息提取Key words
power transformer defects/knowledge graph/generative language model/relation extraction/UIE(universal information extraction)分类
信息技术与安全科学引用本文复制引用
谢国波,刘汉林,林志毅,叶振豪,李斯..面向变压器缺陷知识图谱构建的ET-FSUIE关系抽取模型[J].广东工业大学学报,2025,42(5):77-85,9.基金项目
广东省自然科学基金资助项目(2022A1515012379) （2022A1515012379）
