# KG-BERT: BERT for Knowledge Graph Completion

**Authors:** Liang Yao, Chengsheng Mao, Yuan Luo  
**arXiv:** [1909.03193](https://arxiv.org/abs/1909.03193)  
**Date:** 2019-09-07  
**Summary page:** https://www.summarizepaper.com/fr/paper/1909.03193v2/

## Abstract

Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness. In this work, we propose to use pre-trained language models for knowledge graph completion. We treat triples in knowledge graphs as textual sequences and propose a novel framework named Knowledge Graph Bidirectional Encoder Representations from Transformer (KG-BERT) to model these triples. Our method takes entity and relation descriptions of a triple as input and computes scoring function of the triple with the KG-BERT language model. Experimental results on multiple benchmark knowledge graphs show that our method can achieve state-of-the-art performance in triple classification, link prediction and relation prediction tasks.