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

@inproceedings{MovshovitzAttias2015KBLDAJL,
  title={KB-LDA: Jointly Learning a Knowledge Base of Hierarchy, Relations, and Facts},
  author={Dana Movshovitz-Attias and William W. Cohen},
  booktitle={ACL},
  year={2015}
}
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… CONTINUE READING

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