  Translational distance-based knowledge graph embedding has shown progressive
improvements on the link prediction task, from TransE to the latest
state-of-the-art RotatE. However, N-1, 1-N and N-N predictions still remain
challenging. In this work, we propose a novel translational distance-based
approach for knowledge graph link prediction. The proposed method includes
two-folds, first we extend the RotatE from 2D complex domain to high dimension
space with orthogonal transforms to model relations for better modeling
capacity. Second, the graph context is explicitly modeled via two directed
context representations. These context representations are used as part of the
distance scoring function to measure the plausibility of the triples during
training and inference. The proposed approach effectively improves prediction
accuracy on the difficult N-1, 1-N and N-N cases for knowledge graph link
prediction task. The experimental results show that it achieves better
performance on two benchmark data sets compared to the baseline RotatE,
especially on data set (FB15k-237) with many high in-degree connection nodes.
