Fast parameter learning for Markov logic networks using Bayes nets

Khosravi, Hassan (2013). Fast parameter learning for Markov logic networks using Bayes nets. In: Inductive Logic Programming - 22nd International Conference, ILP 2012, Revised Selected Papers. 22nd International Conference on Inductive Logic Programming, ILP 2012, Dubrovnik, Croatia, (102-115). 17-19 September 2012. doi:10.1007/978-3-642-38812-5_8


Author Khosravi, Hassan
Title of paper Fast parameter learning for Markov logic networks using Bayes nets
Conference name 22nd International Conference on Inductive Logic Programming, ILP 2012
Conference location Dubrovnik, Croatia
Conference dates 17-19 September 2012
Proceedings title Inductive Logic Programming - 22nd International Conference, ILP 2012, Revised Selected Papers   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Series Lecture Notes in Artificial Intelligence
Place of Publication Berlin, Germany
Publisher Springer
Publication Year 2013
Sub-type Fully published paper
DOI 10.1007/978-3-642-38812-5_8
ISBN 9783642388118
9783642388125
ISSN 0302-9743
Volume 7842
Start page 102
End page 115
Total pages 14
Abstract/Summary Markov Logic Networks (MLNs) are a prominent statistical relational model that have been proposed as a unifying framework for statistical relational learning. As part of this unification, their authors proposed methods for converting other statistical relational learners into MLNs. For converting a first order Bayes net into an MLN, it was suggested to moralize the Bayes net to obtain the structure of the MLN and then use the log of the conditional probability table entries to calculate the weight of the clauses. This conversion is exact for converting propositional Markov networks to propositional Bayes nets however, it fails to perform well for the relational case. We theoretically analyze this conversion and introduce new methods of converting a Bayes net into an MLN. An extended imperial evaluation on five datasets indicates that our conversion method outperforms previous methods.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Conference Paper
Collection: Institute for Teaching and Learning Innovation Publications
 
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Created: Thu, 15 Sep 2016, 02:27:08 EST by Hassan Khosravi on behalf of Learning and Research Services (UQ Library)