Learning directed probabilistic logical models from relational data

Article type: Research Article

Authors: Fierens, Daan

Affiliations: Department of Computer Science, Katholieke Universiteit Leuven, Celestijnenlaan 200A, 3001 Heverlee, Belgium. E-mail: daan.fierens@cs.kuleuven.be

Abstract: Data that has a complex relational structure and in which observations are noisy or partially missing poses several challenges to traditional machine learning algorithms. One solution to this problem is the use of so-called probabilistic logical models (models that combine elements of first-order logic with probabilities) and corresponding learning algorithms. In this thesis we focus on directed probabilistic logical models. We show how to represent such models and develop several algorithms to learn such models from data.

Keywords: Probabilistic logic learning, Bayesian networks, decision trees

DOI: 10.3233/AIC-2008-0428

Journal: AI Communications, vol. 21, no. 4, pp. 269-270, 2008