  Knowledge graph embedding methods learn embeddings of entities and relations
in a low dimensional space which can be used for various downstream machine
learning tasks such as link prediction and entity matching. Various graph
convolutional network methods have been proposed which use different types of
information to learn the features of entities and relations. However, these
methods assign the same weight (importance) to the neighbors when aggregating
the information, ignoring the role of different relations with the neighboring
entities. To this end, we propose a relation-aware graph attention model that
leverages relation information to compute different weights to the neighboring
nodes for learning embeddings of entities and relations. We evaluate our
proposed approach on link prediction and entity matching tasks. Our
experimental results on link prediction on three datasets (one proprietary and
two public) and results on unsupervised entity matching on one proprietary
dataset demonstrate the effectiveness of the relation-aware attention.
