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We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols.
Logic and Data Bases, Symposium on Logic and Data Bases, Centre d’études et de recherches de Toulouse, 1977 , Advances in Data Base Theory, New York, 1978. Plemum Press
Hervé Gallaire and Jack Minker, editors · 1978
Earlier work this paper cites.
A prolog technology theorem prover
Mark E. Stickel · 1984
Earlier work this paper cites.
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Allen Van Gelder · 1987
Earlier work this paper cites.
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An approach to combining explanation-based and neural learning algorithms
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Earlier work this paper cites.
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J. Ross Quinlan · 1990
Earlier work this paper cites.
A structured connectionist unification algorithm
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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