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Entity completion for industrial knowledge graph based on zero-shot learning

delete2025-12-09
delete0
PRE
AI
Y
Yin Cai
方
方志军 (Zhijun Fang) *
Y
Yuanyuan Li *
J
Jiacheng Xu
A
Anjie Wang
Z
Zheyi Cheng
DOI：10.1007/s10618-025-01160-0delete
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摘要

摘要

En 中文
知识图谱技术在近年来显著进步，解决了多种工业问题，包括智能决策、缺陷诊断、根本原因分析和质量追溯。然而，由于工业信息化不足，知识图谱中经常包含大量缺失项。在工业知识图谱中，实体补全并不常见。本研究提出了一种NFET-GAN方法，利用零样本学习框架解决工业知识图谱中的实体补全问题。在此方法中，缺失实体的语义特征是从其文本描述中推断得出的。本研究中考虑的一跳邻域特征实际上增强了类内相似性，同时降低了类间相似性。实验数据显示，所提方法优于先前方法，在真实装配领域知识上的平均平均互反排名（Mean Reciprocal Rank）提升了61.4%。该方法具体改进了通用百科全书知识图谱的性能，证明了该方法的普适性。
Keyword:
Intelligent manufacturing
Knowledge graph
Zero-shot learning
Generative adversarial networks

期刊

Data Mining and Knowledge Discovery 封面图
Data Mining and Knowledge Discovery
IF:
4.3
论文数:
221
被引数:
6.0K

机构

S
school of electric and electronic engineering
学者数:
4
论文数: 2
被引数: 0
C
College of Computer Science and Artificial Intelligence
学者数:
95
论文数: 39
被引数: 0
C
College of Computer Science and Technology
学者数:
854
论文数: 298
被引数: 0
S
School of Electronic and Computer Engineering
学者数:
25
论文数: 11
被引数: 0
C
College of Mathematics and Computer Science
学者数:
54
论文数: 28
被引数: 0
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