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Aug, 2024
学习规则引导的子图表示以进行归纳关系预测
Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
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Tianyu Liu, Qitan Lv, Jie Wang, Shuling Yang, Hanzhu Chen
TL;DR
本研究解决了归纳关系预测中目标链接与其他链接的区分问题,提出了一种新颖的单源边向GNN模型REST,以学习规则引导的子图表示。该方法通过初始化仅针对目标链接的边特征来保证挖掘规则的相关性,实验结果表明其在推理性能和子图预处理时间方面具有显著优势。
Abstract
Inductive Relation Prediction
(IRP) -- where entities can be different during training and inference -- has shown great power for completing evolving knowledge graphs. Existing works mainly focus on using
Graph Neural N
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