Learning annotated hierarchies from relational data

The objects in many real-world domains can be organized into hierarchies, where each internal node picks out a category of objects. Given a collection of fea- tures and relations defined over a set of objects, an annotated hierarchy includes a specification of the categories that are most useful for describing each individual feature and relation. We define a generative model for annotated hierarchies and the features and relations that they describe, and develop a Markov chain Monte Carlo scheme for learning annotated hierarchies. We show that our model discov- ers interpretable structure in several real-world data sets.

Authors

• Daniel Roy Univ of Toronto & Vector
• Charles Kemp Joshua Tenenbaum
• Vikash Mansinghka
• Joshua Tenenbaum

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