NDLI: Learning compact Markov logic networks with decision trees

Content Provider	Springer Nature Link
Author	Khosravi, Hassan Schulte, Oliver Hu, Jianfeng Gao, Tianxiang
Copyright Year	2012
Abstract	Statistical-relational learning combines logical syntax with probabilistic methods. Markov Logic Networks (MLNs) are a prominent model class that generalizes both first-order logic and undirected graphical models (Markov networks). The qualitative component of an MLN is a set of clauses and the quantitative component is a set of clause weights. Generative MLNs model the joint distribution of relationships and attributes. A state-of-the-art structure learning method is the moralization approach: learn a set of directed Horn clauses, then convert them to conjunctions to obtain MLN clauses. The directed clauses are learned using Bayes net methods. The moralization approach takes advantage of the high-quality inference algorithms for MLNs and their ability to handle cyclic dependencies. A weakness of moralization is that it leads to an unnecessarily large number of clauses. In this paper we show that using decision trees to represent conditional probabilities in the Bayes net is an effective remedy that leads to much more compact MLN structures. In experiments on benchmark datasets, the decision trees reduce the number of clauses in the moralized MLN by a factor of 5–25, depending on the dataset. The accuracy of predictions is competitive with the models obtained by standard moralization, and in many cases superior.
Starting Page	257
Ending Page	277
Page Count	21
File Format	PDF
ISSN	08856125
Journal	Machine Learning
Volume Number	89
Issue Number	3
e-ISSN	15730565
Language	English
Publisher	Springer US
Publisher Date	2012-07-18
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Markov logic networks Structure learning Bayesian networks Decision trees Control, Robotics, Mechatronics Language Translation and Linguistics Artificial Intelligence (incl. Robotics) Simulation and Modeling Computing Methodologies
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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