@inproceedings{roy2006learning,
title                 = {Learning annotated hierarchies from relational data},
author                = {Roy, Daniel and Kemp, Charles and Mansinghka, Vikash K. and Tenenbaum, Joshua B.},
booktitle             = {NIPS 2006: Advances in Neural Information Processing Systems 19},
pages                 = {1185--1192},
year                  = 2006,
publisher             = {Curran Associates},
url_paper             = {https://papers.nips.cc/paper/3077-learning-annotated-hierarchies-from-relational-data.pdf},
url_link              = {https://papers.nips.cc/paper/3077-learning-annotated-hierarchies-from-relational-data},
abstract              = {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 features 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 discovers interpretable structure in several real-world data sets.},
}

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