Discriminative Markov Logic Network Structure Learning Based on Propositionalization and χ2-Test

摘要：

In this paper we present a bottom-up discriminative algorithm to learn automatically Markov Logic Network structures. Our approach relies on a new propositionalization method that transforms a learning dataset into an approximative representation in the form of boolean tables, from which to construct a set of candidate clauses according to a χ2-test. To compute and choose clauses, we successively use two different optimization criteria, namely pseudo-log-likelihood (PLL) and conditional log-likelihood (CLL), in order to combine the efficiency of PLL optimization algorithms together with the accuracy of CLL ones. First experiments show that our approach outperforms the existing discriminative MLN structure learning algorithms.

关键词： Markov Logic Network Structure Learning Relational Learning l Propositionalization Inductive Logic Programming

作者: Quang-Thang Dinh Matthieu Exbrayat Christel Vrain

作者单位: LIFO Université dOrleans Rue Leonard de Vinci,B.P. 6759,45067 ORLEANS Cedex 2,France

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 24-35

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）