Fast and Robust Link Prediction using Ontology

Anonymous

Abstract: Link Prediction is the task of predicting missing relations between knowledge graph entities (KG). Recent work in link prediction mainly attempted to adapt a model for increasing link prediction accuracy by using more layers in neural network architecture, which heavily rely on computational resources and are not scalable on big KGs. This paper proposes a method of refining knowledge graphs to perform link prediction operations more accurately using relatively fast translational models. Translational link prediction models, such as TransE, TransH, TransD, RotatE, and HAKE, have significantly less complexity than deep learning approaches. Our method uses the ontologies of knowledge graphs to add information as auxiliary nodes to the graph. Then, these auxiliary nodes are connected to ordinary nodes of the KG that contain auxiliary information in their hierarchy. Our experiments show that our method can significantly increase the performance of translational link prediction methods in Hit@10, Mean Rank, Mean Reciprocal Rank.

Paper Type: short