KG-BERT: BERT for Knowledge Graph Completion

Liang Yao; Chengsheng Mao; Yuan Luo

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arXiv · 1909.03193

KG-BERT: BERT for Knowledge Graph Completion

Liang Yao·Chengsheng Mao·Yuan Luo

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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.

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Liang Yao, Chengsheng Mao, Yuan Luo. 2019-09-07. KG-BERT: BERT for Knowledge Graph Completion. https://arxiv.org/abs/1909.03193

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