Abstract
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