Discriminative Markov Logic Network Structure Learning based on Propositionalization and chi 2-test - Archive ouverte HAL Access content directly
Conference Papers Year : 2010

Discriminative Markov Logic Network Structure Learning based on Propositionalization and chi 2-test

Abstract

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 chi2-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.

Dates and versions

hal-00512439 , version 1 (30-08-2010)

Identifiers

Cite

Quang-Thang Dinh, Matthieu Exbrayat, Christel Vrain. Discriminative Markov Logic Network Structure Learning based on Propositionalization and chi 2-test. Advanced Data Mining and Applications - 6th International Conference, ADMA 2010, Nov 2010, Chongqing, China. pp.24-35, ⟨10.1007/978-3-642-17316-5⟩. ⟨hal-00512439⟩
60 View
0 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More