 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).
<meaning-changed>  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 . We also consider an information retrieval task for entity-oriented search. We evaluate an architecture based on a pretrained language model, that exhibits strong generalization to entities not observed during training, and outperforms related state-of-the-art methods (22\% MRR improvement on average).