%0 Generic %1 yao2019kgbert %A Yao, Liang %A Mao, Chengsheng %A Luo, Yuan %D 2019 %K bert completion graph kgbert knowledge lsx:reading-group %T KG-BERT: BERT for Knowledge Graph Completion %U http://arxiv.org/abs/1909.03193 %X 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.

@misc{yao2019kgbert, 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.}, added-at = {2019-10-23T16:33:01.000+0200}, author = {Yao, Liang and Mao, Chengsheng and Luo, Yuan}, biburl = {https://www.bibsonomy.org/bibtex/2f4b42d90cffec94d019bfd9458a10581/nosebrain}, interhash = {276aec6e6e47e10bfc00092aff8291d1}, intrahash = {f4b42d90cffec94d019bfd9458a10581}, keywords = {bert completion graph kgbert knowledge lsx:reading-group}, note = {cite arxiv:1909.03193}, timestamp = {2020-01-05T17:31:16.000+0100}, title = {KG-BERT: BERT for Knowledge Graph Completion}, url = {http://arxiv.org/abs/1909.03193}, year = 2019 }