What is the schema of your knowledge graph?: leveraging knowledge graph embeddings and clustering for expressive taxonomy learning

Large-scale knowledge graphs have become prevalent on the Web and have demonstrated their usefulness for several tasks. One challenge associated to knowledge graphs is the necessity to keep a knowledge graph schema (which is generally manually defined) that accurately reflects the knowledge graph content. In this paper, we present an approach that extracts an expressive taxonomy based on knowledge graph embeddings, linked data statistics and clustering. Our results show that the learned taxonomy is not only able to retain original classes but also identifies new classes, thus giving an up-to-date view of the knowledge graph.