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