Robert Hoehndorf

Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion

Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given KG, i.e., knowledge graph completion (KGC). However, existing KGC methods suffer from two main issues, 1) the false neg…