Abstract
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). Source code and data used in the
experiments can be accessed at https://
github.com/thunlp/MuGNN.