# Improved Knowledge Base Completion by Path-Augmented TransR Model

arXiv: https://arxiv.org/abs/1610.04073 · HTML: https://mlsanity.com/paper/1610.04073/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2016.

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

- [Pre-training and Diagnosing Knowledge Base Completion Models](https://mlsanity.com/paper/2401.15439/) (2024)

## Abstract (arXiv)

Knowledge base completion aims to infer new relations from existing information. In this paper, we propose path-augmented TransR (PTransR) model to improve the accuracy of link prediction. In our approach, we base PTransR model on TransR, which is the best one-hop model at present. Then we regularize TransR with information of relation paths. In our experiment, we evaluate PTransR on the task of entity prediction. Experimental results show that PTransR outperforms previous models.