2020
Probability Calibration for Knowledge Graph Embedding Models
ICLR 2020poster
Knowledge graph embedding research has overlooked the problem of probability calibration. We show popular embedding models are indeed uncalibrated. That means probability estimates associated to predicted triples are unreliable. We present a novel method to calibrate a model when ground truth negati…