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The goal of representation learning of knowledge graph is to encode both entities and relations into a low-dimensional embedding space. 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 methods simply take direct relations between entities into consideration and fails to express high-order structural relationships between entities; (2) these methods simply leverage relation triples of Knowledge Graphs while ignoring a large number of attribute triples that encode rich semantic information. To overcome these limitations, this paper proposes a novel knowledge graph embedding method, named (KANE), which is inspired by the recent developments in graph convolutional networks (GCN). KANE can capture both high-order structural and attribute information of Knowledge …