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Neighbor-Aware Embedding Interaction for Link Prediction

delete2025-10-01
delete0
PRE
AI
D
Di Wu
T
Tong Hui *
M
Ming Liu
H
Honghao Wu
Z
Zhaoqian Zhang
DOI：10.1142/S0218194025500603delete
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摘要

摘要

En 中文
假冒伪劣产品犯罪对消费者安全和市场稳定构成严重威胁，但该领域知识图谱的不完整性阻碍了有效案件分析和决策制定。链接预测（LP）通过捕捉实体结构及语义表示来推断潜在关系，从而缓解图谱不完整性并实现更精准的调查与执法。为实现此目标，现有研究多采用图神经网络聚合实体邻域信息，旨在嵌入过程中捕获结构及语义特征。然而，由于邻域信息存在异质性，直接聚合会降低预测精度。本文提出邻域感知嵌入交互（NEI）模型，通过计算语义与结构邻域间的交互以充分聚合异构邻域信息，进而提升LP性能。具体而言，首先提出依赖方向增强模块，提取高质量语义邻域以改善关系稀疏实体的理解；随后，邻域感知交互模块从语义及结构邻域中捕获有价值隐含信息，丰富邻域嵌入表示；接着，提出邻域感知卷积方法，通过交互整合丰富邻域嵌入向量与三元组头实体及关系嵌入向量，以提升预测性能。我们在三个公开LP数据集上开展实验评估NEI模型的有效性，结果显示，在WN18RR数据集中，NEI模型分别以MRR和Hits@1指标超越基线方法7.3%和5.3%。
Keyword:
Knowledge graph embedding
link prediction
neighbor embedding

期刊

I
International Journal of Software Engineering and Knowledge Engineering
IF:
0.6
论文数:
107
被引数:
543

机构

B
Beijing Police College
学者数:
42
论文数: 32
被引数: 14
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