纺织品表面缺陷知识图谱构建研究

doi:10.3778/j.issn.1002-8331.2406-0087

摘要/Abstract

摘要： 针对纺织品表面缺陷领域的知识存在主观性强、结构分散的问题，研究了面向纺织品表面缺陷的知识图谱构建方法。对纺织品表面缺陷的实体类型和关系种类进行定义，共划分出7种实体类别和26种关系类别，以此为基础标注并构建了纺织品表面缺陷知识图谱数据集。同时，联合基于词典特征增强的实体抽取模型与基于残差注意力增强的双分支关系抽取模型对实体关系进行自动化抽取，构建纺织品表面缺陷知识图谱。实验结果表明，该模型在纺织品表面缺陷领域中的命名实体识别与关系抽取两项任务上的F1值均达到了91%。此外，使用Neo4j数据库对知识图谱进行存储与可视化，并为纺织品表面缺陷知识图谱的下游应用，如智能问答提供了技术支撑。

关键词: 知识图谱, 深度学习, 知识抽取, 残差注意力增强

Abstract: Aiming at the problems of strong subjectivity and dispersed structure in the field of textile surface defects, a knowledge graph construction method for textile surface defects is studied.The entity types and relationship types of textile surface defects are defined, and a total of 7 entity categories and 26 relationship categories are divided. Based on this, a textile surface defect knowledge graph data set is constructed. At the same time, the entity extraction model based on dictionary feature enhancement and the dual-branch relationship extraction model based on residual attention enhancement are combined to automatically extract entity relationships and construct a knowledge graph of textile surface defects. Experimental results show that the F1 value of this model reaches 91% in both named entity recognition and relationship extraction tasks in the field of textile surface defects. In addition, the Neo4j database is used to store and visualize the knowledge graph, and provides technical support for downstream applications of the textile surface defect knowledge graph, such as intelligent question answering.

Key words: knowledge graph, deep learning, knowledge extraction, residual attention enhancement

姜晓恒, 吕鹏帅, 刘允, 卢洋, 张坤丽, 徐明亮. 纺织品表面缺陷知识图谱构建研究[J]. 计算机工程与应用, 2025, 61(19): 292-301.

JIANG Xiaoheng, LYU Pengshuai, LIU Yun, LU Yang, ZHANG Kunli, XU Mingliang. Research on Construction of Knowledge Graph of Textile Surface Defect[J]. Computer Engineering and Applications, 2025, 61(19): 292-301.

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