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.
@article{arxiv.1909.03193,
title = {KG-BERT: BERT for Knowledge Graph Completion},
author = {Liang Yao and Chengsheng Mao and Yuan Luo},
journal= {arXiv preprint arXiv:1909.03193},
year = {2019}
}