Improving the Utility of Knowledge Graph Embeddings with Calibration

2 Apr 2020Tara SafaviDanai KoutraEdgar Meij

This paper addresses machine learning models that embed knowledge graph entities and relationships toward the goal of predicting unseen triples, which is an important task because most knowledge graphs are by nature incomplete. We posit that while offline link prediction accuracy using embeddings has been steadily improving on benchmark datasets, such embedding models have limited practical utility in real-world knowledge graph completion tasks because it is not clear when their predictions should be accepted or trusted... (read more)

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