  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.
