Statistical relational learning : Structure learning for Markov logic networks
type: Dataset
description: A Markov Logic Network is composed of a set of weighted first-order logic formulas. In this dissertation we propose several methods to learn a MLN structure from a relational dataset. These methods are of two kinds: methods based on propositionalization and methods based on Graph of Predicates. The methods based on propositionalization are based on the idea of building a set of candidate clauses from sets of dependent variable literals. In order to find such sets of dependent variable literals, we use a propositionalization technique to transform relational information in the dataset into boolean tables, that are then provided as contingency tables for tests of dependence. Two propositionalization methods are proposed, from which three learners have been developed, that handle both generative and discriminative learning. We then introduce the concept of Graph of Predicates, which synthethises the binary relations between the predicates of a domain. Candidate clauses can be quickly and easily generated by simply finding paths in the graph and then variabilizing them. Based on this Graph, two learners have been developed, that handle both generative and discriminative learning.
identifier: NNT: 2011ORLE2047
issued: 2026-05-22T04:11:02.054023
language: en
modified: 2026-05-22T04:11:02.054029
contact point: Organization
  fn: CCSD
keyword: apprentissage-de-structure
keyword: apprentissage-statistique-relationnel
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: markov-logic-networks
keyword: reseaux-logiques-de-markov
keyword: statistical-relational-learning
keyword: structure-learning
keyword: theses
landing page: https://theses.hal.science/tel-00659738

Statistical relational learning : Structure learning for Markov logic networks
type: Distribution
format: HTML
issued: 2026-05-22T04:11:02.067174
modified: 2026-05-22T04:11:02.033811
access URL: https://theses.hal.science/tel-00659738

test_moissonnage_selune
type: Agent