Learning Systems of Concepts with an Infinite Relational Model

Charles Kemp, Joshua Tenenbaum, Thomas Griffiths, Takeshi Yamada, Naonori Ueda

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.

Subjects: 12. Machine Learning and Discovery

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