  Learning knowledge graph (KG) embeddings has received increasing attention in
recent years. Most embedding models in literature interpret relations as linear
or bilinear mapping functions to operate on entity embeddings. However, we find
that such relation-level modeling cannot capture the diverse relational
structures of KGs well. In this paper, we propose a novel edge-centric
embedding model TransEdge, which contextualizes relation representations in
terms of specific head-tail entity pairs. We refer to such contextualized
representations of a relation as edge embeddings and interpret them as
translations between entity embeddings. TransEdge achieves promising
performance on different prediction tasks. Our experiments on benchmark
datasets indicate that it obtains the state-of-the-art results on
embedding-based entity alignment. We also show that TransEdge is complementary
with conventional entity alignment methods. Moreover, it shows very competitive
performance on link prediction.
