Learning systems of concepts with an infinite relational model

Authors:
Charles Kemp;Joshua B. Tenenbaum;Thomas L. Griffiths;Takeshi Yamada;Naonori Ueda
Affiliations:
Department of Brain and Cognitive Science, Massachusetts Institute of Technology;Department of Brain and Cognitive Science, Massachusetts Institute of Technology;Department of Cognitive and Linguistic Sciences, Brown University;NTT Communication Science Laboratories;NTT Communication Science Laboratories
Venue:
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Year:
2006

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Abstract

Relationships between concepts account for a large proportion of semantic knowledge. We present a nonparametric Bayesian model that discovers systems of related concepts. Given data involving several sets of entities, our model discovers the kinds of entities in each set and the relations between kinds that are possible or likely. We apply our approach to four problems: clustering objects and features, learning ontologies, discovering kinship systems, and discovering structure in political data.