Graph neural networks (GNNs) have emerged as a powerful paradigm for
embedding-based entity alignment due to their capability of identifying
isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart
entities usually have non-isomorphic neighborhood structures, which easily
causes GNNs to yield different representations for them. To tackle this
problem, we propose a new KG alignment network, namely AliNet, aiming at
mitigating the non-isomorphism of neighborhood structures in an end-to-end
manner. As the direct neighbors of counterpart entities are usually dissimilar
due to the schema heterogeneity, AliNet introduces distant neighbors to expand
the overlap between their neighborhood structures. It employs an attention
mechanism to highlight helpful distant neighbors and reduce noises. Then, it
controls the aggregation of both direct and distant neighborhood information
using a gating mechanism. We further propose a relation loss to refine entity
representations. We perform thorough experiments with detailed ablation studies
and analyses on five entity alignment datasets, demonstrating the effectiveness
of AliNet.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI
2020