Generative Structure Learning for Markov Logic Networks Based on Graph of Predicates

Abstract : In this paper we present a new algorithm for generative learning of the structure of Markov Logic Networks. This algorithm relies on a graph of predicates, which summarizes the links existing between predicates and relational information between ground atoms in the training database. Candidate clauses are produced by the mean of a heuristical variabilization technique. According to our first experiments, this approach appears to be promising.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00584418
Contributor : Matthieu Exbrayat <>
Submitted on : Friday, April 8, 2011 - 3:10:08 PM
Last modification on : Tuesday, May 14, 2019 - 11:04:18 AM

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  • HAL Id : hal-00584418, version 1

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Quang-Thang Dinh, Matthieu Exbrayat, Christel Vrain. Generative Structure Learning for Markov Logic Networks Based on Graph of Predicates. IJCAI 2011, Jul 2011, Barcelona, Spain. pp.1249-1254. ⟨hal-00584418⟩

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