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Papers/KG-BERT: BERT for Knowledge Graph Completion

KG-BERT: BERT for Knowledge Graph Completion

Liang Yao, Chengsheng Mao, Yuan Luo

2019-09-07Knowledge GraphsTriple ClassificationKnowledge Graph CompletionPredictionRelation PredictionLanguage ModellingLink Prediction
PaperPDFCodeCodeCode(official)

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.

Results

TaskDatasetMetricValueModel
Link PredictionUMLSHits@100.99KG-BERT
Link PredictionUMLSMR1.47KG-BERT
Link PredictionWN18RRHits@100.524KG-BERT
Link PredictionWN18RRMR97KG-BERT
Link PredictionFB15k-237Hits@100.42KG-BERT
Link PredictionFB15k-237MR153KG-BERT

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