KG-BERT: BERT for Knowledge Graph Completion

Code

• github.com/yao8839836/kg-bert
  OfficialIn paperpytorch★ 0
• github.com/gychant/CSKMTermDefn
  pytorch★ 6
• github.com/ManasRMohanty/DS5500-capstone
  pytorch★ 0

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.

Tasks

Benchmark Results

Reproductions