  Knowledge graphs have attracted lots of attention in academic and industrial
environments. Despite their usefulness, popular knowledge graphs suffer from
incompleteness of information, especially in their type assertions. This has
encouraged research in the automatic discovery of entity types. In this
context, multiple works were developed to utilize logical inference on
ontologies and statistical machine learning methods to learn type assertion in
knowledge graphs. However, these approaches suffer from limited performance on
noisy data, limited scalability and the dependence on labeled training samples.
In this work, we propose a new unsupervised approach that learns to categorize
entities into a hierarchy of named groups. We show that our approach is able to
effectively learn entity groups using a scalable procedure in noisy and sparse
datasets. We experiment our approach on a set of popular knowledge graph
benchmarking datasets, and we publish a collection of the outcome group
hierarchies.
