KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts


Many existing knowledge bases (KBs), including Freebase, Yago, and NELL, rely on a fixed ontology, given as an input to the system, which defines the data to be cataloged in the KB, i.e., a hierarchy of categories and relations between them. The system then extracts facts that match the predefined ontology. We propose an unsupervised model that jointly learns a latent ontological structure of an input corpus, and identifies facts from the corpus that match the learned structure. Our approach combines mixed membership stochastic block models and topic models to infer a structure by jointly mod-eling text, a latent concept hierarchy, and latent semantic relationships among the entities mentioned in the text. As a case study, we apply the model to a corpus of Web documents from the software domain , and evaluate the accuracy of the various components of the learned ontology.

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