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

By Dana Movshovitz-Attias, William W. Cohen. 2015

18 citations recorded by the source corpus.

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 modeling 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.

Subfields: Artificial Intelligence, Natural Language Processing, Graph Learning

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Citations in the corpus

A Survey of Machine Learning for Big Code and Naturalness (2017, background): “uences Graphical Model API Mining Murali et al. [135] Sketch Synthesis Graphical Model Sketch Mining Murali et al. [136] API Usage Errors Graphical Model Defect Prediction Movshovitz-Attias and Cohen [134] Tokens Graphical Model Knowledge-BaseMining Nguyen et al. [143] API Usage Distributed API Mini”