  The Link Prediction is the task of predicting missing relations between
entities of the knowledge graph. Recent work in link prediction has attempted
to provide a model for increasing link prediction accuracy by using more layers
in neural network architecture. In this paper, we propose a novel method of
refining the knowledge graph so that link prediction operation can be performed
more accurately using relatively fast translational models. Translational link
prediction models, such as TransE, TransH, TransD, have less complexity than
deep learning approaches. Our method uses the hierarchy of relationships and
entities in the knowledge graph to add the entity information as auxiliary
nodes to the graph and connect them to the nodes which contain this information
in their hierarchy. Our experiments show that our method can significantly
increase the performance of translational link prediction methods in H@10, MR,
MRR.
