Learning systems of concepts with an infinite relational model
AAAI(2006)
摘要
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.
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关键词
large proportion,kinship system,political data,nonparametric bayesian model,semantic knowledge,clustering object,related concept,infinite relational model,bayesian model,domain theory,relational model,unsupervised learning,arsenic
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