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ICMLA

2010

2010

Markov Logic Networks (MLNs) combine Markov Networks and first-order logic by attaching weights to firstorder formulas and viewing them as templates for features of Markov Networks. Learning a MLN can be decomposed into structure learning and weights learning. In this paper, we present a heuristic-based algorithm to learn discriminative MLN structures automatically, directly from a training dataset. The algorithm heuristically transforms the relational dataset into boolean tables from which to build candidate clauses for learning the final MLN. Comparisons to the state-of-the-art structure learning algorithms for MLNs in the three real-world domains show that the proposed algorithm outperforms them in terms of the conditional log likelihood (CLL), and the area under the precision-recall curve (AUC). Keywords-Markov Logic Network, Structure Learning, Discriminative learning, Relational Learning.

Related Content

Added |
12 Feb 2011 |

Updated |
12 Feb 2011 |

Type |
Journal |

Year |
2010 |

Where |
ICMLA |

Authors |
Quang-Thang Dinh, Matthieu Exbrayat, Christel Vrain |

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