Learning Systems of Concepts with an Infinite Relational Model

Proceedings of the AAAI Conference on Artificial Intelligence, 21

Volume

Technical Papers

Machine Learning

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.