  Knowledge graph embedding (KGE) models learn the representation of entities
and relations in knowledge graphs. Distance-based methods show promising
performance on link prediction task, which predicts the result by the distance
between two entity representations. However, most of these methods represent
the head entity and tail entity separately, which limits the model capacity. We
propose two novel distance-based methods named InterHT and InterHT+ that allow
the head and tail entities to interact better and get better entity
representation. Experimental results show that our proposed method achieves the
best results on ogbl-wikikg2 dataset.
