Learning compact Markov logic networks with decision trees

Khosravi, Hassan, Schulte, Oliver, Hu, Jianfeng and Gao, Tianxiang (2012) Learning compact Markov logic networks with decision trees. Machine Learning, 89 3: 257-277. doi:10.1007/s10994-012-5307-6

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Author Khosravi, Hassan
Schulte, Oliver
Hu, Jianfeng
Gao, Tianxiang
Title Learning compact Markov logic networks with decision trees
Journal name Machine Learning   Check publisher's open access policy
ISSN 0885-6125
Publication date 2012-12-01
Year available 2012
Sub-type Article (original research)
DOI 10.1007/s10994-012-5307-6
Open Access Status Not yet assessed
Volume 89
Issue 3
Start page 257
End page 277
Total pages 21
Place of publication New York, United States
Publisher Springer
Language eng
Formatted abstract
Statistical-relational learning combines logical syntax with probabilistic methods. Markov Logic Networks (MLNs) are a prominent model class that generalizes both firstorder 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-ofthe-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.
Keyword Bayesian networks
Decision trees
Markov logic networks
Structure learning
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Teaching and Learning Innovation Publications
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
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Created: Thu, 15 Sep 2016, 02:17:38 EST by Hassan Khosravi on behalf of Learning and Research Services (UQ Library)