Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning
This work addresses entity alignment for knowledge graphs, an incremental improvement in structure-based methods.
The paper tackles the challenge of aligning entities across knowledge graphs with complex structures by proposing UniEA, a method that unifies dual-space embeddings in Euclidean and hyperbolic spaces, achieving state-of-the-art performance on benchmark datasets.
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.