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The Link Prediction is the task of predicting missing relations betweenentities of the knowledge graph. Recent work in link prediction has attemptedto provide a model for increasing link prediction accuracy by using more layersin neural network architecture. In this paper, we propose a novel method ofrefining the knowledge graph so that link prediction operation can be performedmore accurately using relatively fast translational models. Translational linkprediction models, such as TransE, TransH, TransD, have less complexity thandeep learning approaches. Our method uses the hierarchy of relationships andentities in the knowledge graph to add the entity information as auxiliarynodes to the graph and connect them to the nodes which contain this informationin their hierarchy. Our experiments show that our method can significantlyincrease the performance of translational link prediction methods in H@10, MR,MRR.

KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods