Knowledge graph completion can complete knowledge by predicting missing facts, which is a increasingly hot research topic in knowledge graph construction. Prevalent approaches propose to embed knowledge graphs in a lowdimensional vector space and use these embedding to predict, but they neglect either semantic information or graph structures. We propose a new approach to knowledge graph completion named as ATTWALK, which learns embedding by exploiting both structural and semantic features of a knowledge graph. This is achieved by leveraging a key insight that an entities’ embedding is influenced by its multi-hop neighbors’, which can be further distinguished by their semantic importance to the entity. ATTWALK orchestrates a two-step workflow by first evaluating neighbors’ semantic weights using graph attention networks for each entity, then exploring the entities’ local structural features by performing a semantic weight guided walk. We evaluate ATTWALK by conducting extensive experiments, which show that ATTWALK outperforms 12 representative approaches on average across 3 publicly available datasets.