  Entity alignment (EA) is the task to discover entities referring to the same
real-world object from different knowledge graphs (KGs), which is the most
crucial step in integrating multi-source KGs. The majority of the existing
embeddings-based entity alignment methods embed entities and relations into a
vector space based on relation triples of KGs for local alignment. As these
methods insufficiently consider the multiple relations between entities, the
structure information of KGs has not been fully leveraged. In this paper, we
propose a novel framework based on Relation-aware Graph Attention Networks to
capture the interactions between entities and relations. Our framework adopts
the self-attention mechanism to spread entity information to the relations and
then aggregate relation information back to entities. Furthermore, we propose a
global alignment algorithm to make one-to-one entity alignments with a
fine-grained similarity matrix. Experiments on three real-world cross-lingual
datasets show that our framework outperforms the state-of-the-art methods.
