We present a method for learning representations of entities, that uses a Transformer-based architecture as an entity encoder, and link prediction training on a knowledge graph with textual entity descriptions . 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 present a method for learning representations of entities, that uses a Transformer-based architecture as an entity encoder, and link prediction training on a knowledge graph with textual entity descriptions . 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).
Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and metadata annotation. Regardless of whether they are built manually by domain experts or with automatic pipelines, KGs are often incomplete. Recent work has begun to explore the use of textual descriptions available in knowledge graphs to learn vector representations of entities in order to preform link prediction. However, the extent to which these representations learned for link prediction generalize to other tasks is unclear. This is important given the cost of learning such representations. Ideally, we would prefer representations that do not need to be trained again when transferring to a different task, while retaining reasonable performance. In this work, we propose a holistic evaluation protocol for entity representations learned via a link prediction objective. We consider the inductive link prediction and entity classification tasks, which involve entities not seen during training , outperforming related state-of-the-art methods (22\% MRR improvement on average).