Abstract: Temporal knowledge graphs(TKG) have great potential of application in many fields,such as recommender systems,search engine and natural language processing,but the incompleteness of TKG limites its application,so it is important to study link prediction model on TKG.Most existing methods focus on TKG completion and can't predict future facts.This paper proposes a link prediction model on TKG,which is based on bidirectionally aggregating neighborhoods and global aware.On the one hand,the proposed model independently aggregates entity's recently active and positive behavior and models their temporal evolution by recurrent neural network (RNN).On the other hand,it captures the chronic behavior patterns of entities by global aware module.Experimental results on four benchmark datasets show that our proposed method can improve the performance of forecasting future facts.

Key words: Temporal knowledge graph, Link prediction, Recurrent neural network