## Structure learning of Markov logic networks through iterated local search (2008)

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Venue: | Proc. ECAI’08 |

Citations: | 17 - 2 self |

### BibTeX

@INPROCEEDINGS{Biba08structurelearning,

author = {Marenglen Biba and Stefano Ferilli and Floriana Esposito},

title = {Structure learning of Markov logic networks through iterated local search},

booktitle = {Proc. ECAI’08},

year = {2008}

}

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### Abstract

Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling complexity, and statistical AI on handling uncertainty. Markov Logic Networks (MLNs) are a powerful representation that combine Markov Networks (MNs) and first-order logic by attaching weights to first-order formulas and viewing these as templates for features of MNs. State-of-theart structure learning algorithms of MLNs maximize the likelihood of a relational database by performing a greedy search in the space of candidates. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for learning MLNs structure, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. The algorithm focuses the search not on the full space of solutions but on a smaller subspace defined by the solutions that are locally optimal for the optimization engine. We show through experiments in two real-world domains that the proposed approach improves accuracy and learning time over the existing state-of-the-art algorithms. 1

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11 |
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Citation Context ... of relevant clauses. The search is driven by the performance obtained by a support vector machine based on the resulting kernel. The authors showed that kFOIL improves over nFOIL. Recently, in TFOIL =-=[18]-=-, Tree Augmented Naïve Bayes, a generalization of Naïve Bayes was integrated with FOIL and it was shown that TFOIL outperforms nFOIL. The most closely related approach to the DSL algorithm is nFOIL (a... |

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Citation Context ...characterized by both uncertainty and complex relational structure. Statistical learning focuses on the former, and relational learning on the latter. Probabilistic Inductive Logic Programming (PILP) =-=[7]-=- or Statistical Relational Learning [10] aim at combining the power of both. PILP and SRL can be viewed as combining ILP principles (such as refinement operators) with statistical learning. One of the... |

5 |
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Citation Context ...able. The dataset contains a total of 70367 tuples (true and false ground atoms, with the remainder assumed false). 6.2 Systems and Methodology We implemented the DSL algorithm in the Alchemy package =-=[15]-=-. We used the implementation of L-BFGS and Lazy-MC-SAT in Alchemy to learn maximum WPLL weights and compute CLL during clause search. Regarding parameter learning, we compared our algorithm performanc... |

3 |
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Citation Context ...that employ statistical models such as maximum entropy modeling in [4] and logistic regression in [26]. These approaches can be computationally very expensive. A simpler approach that integrates FOIL =-=[29]-=- and Naïve Bayes is nFOIL proposed in [17]. This approach interleaves the steps of generating rules and scoring them through CLL. In another work [2] these steps are coupled by scoring the clauses thr... |

2 |
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2 |
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Citation Context ... parameters taken from [24] in terms of number of variables and literals per clause, while for DSL we did not optimize any parameter. Regarding question (Q5), the goal was whether previous results of =-=[22]-=- carry on to MLNs, that on small datasets generative approaches can perform better than discriminative ones. The UW-CSE dataset with a total of 2673 tuples can be considered of much smaller size compa... |