KG-BERT: BERT for Knowledge Graph Completion

Understand

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.