用双空间嵌入提升跨知识图谱实体对齐精度
Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning
- 在欧氏与双曲空间同步学习图结构嵌入
- 对比学习缓解相似邻域导致的误对齐问题
- 适用于复杂层级结构的知识图谱对齐
实体对齐旨在匹配不同知识图谱中的相同实体。基于图神经网络的方法在欧氏空间中取得了良好效果，但知识图谱常包含局部与层次化等复杂结构，单一空间难以高效表示。本文提出UniEA方法，通过统一双空间嵌入以保留知识图谱内在结构。具体地，同时在欧氏与双曲空间学习图结构嵌入，最大化两空间间嵌入的一致性；并采用对比学习缓解由相似实体引起的误对齐问题，使同图中相似邻接实体的嵌入距离不过近。大量基准数据集上的实验表明，该方法在基于结构的实体对齐任务中达到领先性能。
原文摘要 · Abstract (English)
Entity alignment aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contain complex structures, including both local and hierarchical ones, which make it challenging to efficiently represent them within a single space. In this paper, we proposed a novel method UniEA, which unifies dual-space embedding to preserve the intrinsic structure of KGs. Specifically, we learn graph structure embedding in both Euclidean and hyperbolic spaces simultaneously to maximize the consistency between the embedding in both spaces. Moreover, we employ contrastive learning to mitigate the misalignment issues caused by similar entities, where embedding of similar neighboring entities within the KG become too close in distance. Extensive experiments on benchmark datasets demonstrate that our method achieves state-of-the-art performance in structure-based EA. Our code is available at https://github.com/wonderCS1213/UniEA.
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