In this paper, we propose an unsupervised model that learns a latent hierarchical structure of categories from an input corpus, learns latent semantic relations between categories, and also identifies facts from the corpus that match the learned structure.
the model learns both the schema for a KB, and a set of facts that are related to that schema, thus combining the processes of KB population and ontology construction.