  Entity alignment typically suffers from the issues of structural
heterogeneity and limited seed alignments. In this paper, we propose a novel
Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented
knowledge graph (KG) embeddings by robustly encoding two KGs via multiple
channels. Each channel encodes KGs via different relation weighting schemes
with respect to self-attention towards KG completion and cross-KG attention for
pruning exclusive entities respectively, which are further combined via pooling
techniques. Moreover, we also infer and transfer rule knowledge for completing
two KGs consistently. MuGNN is expected to reconcile the structural differences
of two KGs, and thus make better use of seed alignments. Extensive experiments
on five publicly available datasets demonstrate our superior performance (5%
Hits@1 up on average).
