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

2019-11-12 | |  52 |   44 |   0
Abstract In this paper we present a new algorithm for generatively learning the structure of Markov Logic Networks. This algorithm relies on a graph of predicates, which summarizes the links existing between predicates and on relational information between ground atoms in the training database. Candidate clauses are produced by means of a heuristical variabilization technique. According to our ?rst experiments, this approach appears to be promising.

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