  Entity alignment is the task of linking entities with the same real-world
identity from different knowledge graphs (KGs), which has been recently
dominated by embedding-based methods. Such approaches work by learning KG
representations so that entity alignment can be performed by measuring the
similarities between entity embeddings. While promising, prior works in the
field often fail to properly capture complex relation information that commonly
exists in multi-relational KGs, leaving much room for improvement. In this
paper, we propose a novel Relation-aware Dual-Graph Convolutional Network
(RDGCN) to incorporate relation information via attentive interactions between
the knowledge graph and its dual relation counterpart, and further capture
neighboring structures to learn better entity representations. Experiments on
three real-world cross-lingual datasets show that our approach delivers better
and more robust results over the state-of-the-art alignment methods by learning
better KG representations.
