  Entity alignment aims at integrating heterogeneous knowledge from different
knowledge graphs. Recent studies employ embedding-based methods by first
learning the representation of Knowledge Graphs and then performing entity
alignment via measuring the similarity between entity embeddings. However, they
failed to make good use of the relation semantic information due to the
trade-off problem caused by the different objectives of learning knowledge
embedding and neighborhood consensus. To address this problem, we propose
Relational Knowledge Distillation for Entity Alignment (RKDEA), a Graph
Convolutional Network (GCN) based model equipped with knowledge distillation
for entity alignment. We adopt GCN-based models to learn the representation of
entities by considering the graph structure and incorporating the relation
semantic information into GCN via knowledge distillation. Then, we introduce a
novel adaptive mechanism to transfer relational knowledge so as to jointly
learn entity embedding and neighborhood consensus. Experimental results on
several benchmarking datasets demonstrate the effectiveness of our proposed
model.
