Knowledge graph embedding by relational rotation and complex convolution for link prediction

Thanh Le, Nam Le, Bac Le

Abstract: Highlights•Geometric-based models are intuitive but do not have expressive powers.•Interaction features enhance valuable information for rotation transformation.•Interactions between entities and relations are obtained from complex convolution.•Two ways of designing convolution exploit features from different perspectives.•Type constraints help to improve the model performance significantly.