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We consider the problem of embedding entities and relations of knowledge bases in low-dimensional vector spaces.
Parafac: parallel factor analysis
Harshman, Richard A. and Lundy, Margaret E · 1994
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Learning systems of concepts with an infinite relational model
Kemp, Charles, Tenenbaum, Joshua B., Griffiths, Thomas L., Yamada, Takeshi, and Ueda, Naonori · 2006
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Statistical predicate invention
Kok, Stanley and Domingos, Pedro · 2007
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Modelling relational data using bayesian clustered tensor factorization
Sutskever, Ilya, Salakhutdinov, Ruslan, and Tenenbaum, Josh · 2009
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Learning structured embeddings of knowledge bases
Bordes, A., Weston, J., Collobert, R., and Bengio, Y · 2011
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A semantic matching energy function for learning with multi-relational data
Bordes, Antoine, Glorot, Xavier, Weston, Jason, and Bengio, Yoshua · 2013
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Learning new facts from knowledge bases with neural tensor networks and semantic word vectors
Chen, Danqi, Socher, Richard, Manning, Christopher D, and Ng, Andrew Y · 2013
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Efficient estimation of word representations in vector space
Mikolov, Tomas, Chen, Kai, Corrado, Greg, and Dean, Jeffrey · 2013
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