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

TL;DR

This paper introduces KGRefiner, a method that refines knowledge graphs by adding hierarchical auxiliary nodes, significantly improving the accuracy of translational link prediction models like TransE, TransH, and TransD.

Contribution

It proposes a novel graph refinement technique leveraging hierarchy information to enhance the performance of simple translational link prediction models.

Findings

Significant increase in H@10, MR, MRR metrics

Improved accuracy with relatively fast models

Effective use of hierarchy in knowledge graphs

Abstract

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.