 We demonstrate that our approach can be applied effectively for link prediction in different inductive settings involving entities not seen during training , outperforming related state-of-the-art methods (22\% MRR improvement  on average).
<clarity>  We demonstrate that our approach can be applied effectively for link prediction in different inductive settings involving entities not seen during training , outperforming related state-of-the-art methods (22\% MRR improvement  on average).
 We demonstrate that our approach can be applied effectively for link prediction in different inductive settings involving entities not seen during training , outperforming related state-of-the-art methods (22\% MRR improvement in link prediction on average).