  The goal of representation learning of knowledge graph is to encode both
entities and relations into a low-dimensional embedding spaces. Many recent
works have demonstrated the benefits of knowledge graph embedding on knowledge
graph completion task, such as relation extraction. However, we observe that:
1) existing method just take direct relations between entities into
consideration and fails to express high-order structural relationship between
entities; 2) these methods just leverage relation triples of KGs while ignoring
a large number of attribute triples that encoding rich semantic information. To
overcome these limitations, this paper propose a novel knowledge graph
embedding method, named KANE, which is inspired by the recent developments of
graph convolutional networks (GCN). KANE can capture both high-order structural
and attribute information of KGs in an efficient, explicit and unified manner
under the graph convolutional networks framework. Empirical results on three
datasets show that KANE significantly outperforms seven state-of-arts methods.
Further analysis verify the efficiency of our method and the benefits brought
by the attention mechanism.
