  Entity alignment is a viable means for integrating heterogeneous knowledge
among different knowledge graphs (KGs). Recent developments in the field often
take an embedding-based approach to model the structural information of KGs so
that entity alignment can be easily performed in the embedding space. However,
most existing works do not explicitly utilize useful relation representations
to assist in entity alignment, which, as we will show in the paper, is a simple
yet effective way for improving entity alignment. This paper presents a novel
joint learning framework for entity alignment. At the core of our approach is a
Graph Convolutional Network (GCN) based framework for learning both entity and
relation representations. Rather than relying on pre-aligned relation seeds to
learn relation representations, we first approximate them using entity
embeddings learned by the GCN. We then incorporate the relation approximation
into entities to iteratively learn better representations for both. Experiments
performed on three real-world cross-lingual datasets show that our approach
substantially outperforms state-of-the-art entity alignment methods.
