TY - GEN
T1 - Learning systems of concepts with an infinite relational model
AU - Kemp, Charles
AU - Tenenbaum, Joshua B.
AU - Griffiths, Thomas L.
AU - Yamada, Takeshi
AU - Ueda, Naonori
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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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M3 - Conference contribution
AN - SCOPUS:33750696648
SN - 1577352815
SN - 9781577352815
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 381
EP - 388
BT - Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
T2 - 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06
Y2 - 16 July 2006 through 20 July 2006
ER -