Knowledge　Graph　Completion　(KGC)　has　been　an　active　research　topic　in　recent　years,　which　is　the　task　of　predicting　missing　links　based　on　known　triples　of　knowledge　graphs.　Some　recent　work　has　shown　that　graph　neural　networks　(GNNs)　using　graph　structure　can　perform　well　on　KGC.　These　models　learn　information　from　entities　and　relations　within　the　subject＇s　neighborhood　and　update　the　representation　through　a　message　passing　mechanism.　However,　existing　GNN　models　rarely　include　the　modeling　of　relational　information,　and　they　tend　to　represent　and　learn　nodes　through　complex　networks,　ignoring　the　underlying　semantic　information　between　relations.　In　this　work,　we　propose　a　global　relationship-assisted　graph　attention　network.　It　not　only　models　entities　but　also　builds　directed　graph　structures　and　updates　the　representation　of　relations　between　different　relations.　Specifically,　the　strongly　correlated　neighboring　relations　are　identified　for　aggregation　by　an　attention　function　based　on　the　information　and　spatial　domains.　We　also　use　a　learnable　nonlinear　function　to　activate　the　attention　values,　allowing　the　model　to　adaptively　aggregate　information.　Experiments　show　that　GRA-GAT　has　a　very　advanced　performance　on　link　prediction　tasks.　©　2022　IEEE.