Abstract: Knowledge graph is a popular research area related to artificial intelligence.Knowledge graph completion is the completion of missing entities given head or tail entities and corresponding relations.Translation models (such as TransE,TransH and TransR) are one of the most commonly used completion methods.However,most of the existing completion models ignore the feature of the importance of the triplets in the knowledge graph during the completion process.This paper proposes a novel knowledge graph completion model,ImpTransE,which takes into account the importance feature in triplets,and designs the entity importance ranking method KGNodeRank and the multi-grained relation importance estimation method MG-RIE,to estimate the entity importance and relation importance,respectively.Specifically,the KGNodeRank method estimates the entity node importance ranking by considering both the importance of the associated nodes and the probability that their importance is transmitted,while the MG-RIE method considers multi-order relation importance to provide a reasonable estimate of the overall importance of the relation.ImpTransE takes into account the entity importance and relation importance features of triplets,so that differentle-vels of attention are given to different triplets during the learning process,which improves the learning performance of the ImpTransE model and thus achieves better completion performance.Experimental results show that ImpTransE model has the best completion performance in most of the metrics on the two knowledge graph datasets compared with the five comparison models,and completion performance of different datasets is consistently improved.

Key words: Entity importance, Knowledge graph, Link prediction, Relation importance