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

arXiv: https://arxiv.org/abs/2106.14233 · HTML: https://mlsanity.com/paper/2106.14233/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 27 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network](https://mlsanity.com/paper/1712.02121/) (2017)
- [End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion](https://mlsanity.com/paper/1811.04441/) (2018)
- [Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction](https://mlsanity.com/paper/1911.09419/) (2019)
- [Knowledge Graph Embedding for Link Prediction: A Comparative Analysis](https://mlsanity.com/paper/2002.00819/) (2020)
- [Question Answering with Subgraph Embeddings](https://mlsanity.com/paper/1406.3676/) (2014)
- [TransEdge: Translating Relation-contextualized Embeddings for Knowledge Graphs](https://mlsanity.com/paper/2004.13579/) (2020)
- [Differentiating Concepts and Instances for Knowledge Graph Embedding](https://mlsanity.com/paper/1811.04588/) (2018)
- [Open Question Answering with Weakly Supervised Embedding Models](https://mlsanity.com/paper/1404.4326/) (2014)
- [Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network](https://mlsanity.com/paper/2106.06555/) (2021)
- [Biomedical Knowledge Graph Refinement with Embedding and Logic Rules](https://mlsanity.com/paper/2012.01031/) (2020)

## Built on by (most cited first, 1 of 1)

- [DeepRefine: Agentic Knowledge Refinement via Reinforcement Learning](https://mlsanity.com/paper/2605.10488/) (2026)

## Abstract (arXiv)

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.