Structure learning for Markov Logic Networks with many descriptive attributes

Khosravi, Hassan, Schulte, Oliver, Man, Tong, Xu, Xiaoyuan and Bina, Bahareh (2010). Structure learning for Markov Logic Networks with many descriptive attributes. In: AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference. 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10, Atlanta, GA, United States, (487-493). 11-15 July 2010.

Author Khosravi, Hassan
Schulte, Oliver
Man, Tong
Xu, Xiaoyuan
Bina, Bahareh
Title of paper Structure learning for Markov Logic Networks with many descriptive attributes
Conference name 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10
Conference location Atlanta, GA, United States
Conference dates 11-15 July 2010
Proceedings title AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference
Series Proceedings of the National Conference on Artificial Intelligence
Place of Publication Palo Alto, CA, United States
Publisher Association for the Advancement of Artificial Intelligence
Publication Year 2010
Sub-type Fully published paper
Open Access Status Not yet assessed
ISBN 9781577354642
Volume 1
Start page 487
End page 493
Total pages 7
Abstract/Summary Many machine learning applications that involve relational databases incorporate first-order logic and probability. Markov Logic Networks (MLNs) are a prominent statistical relational model that consist of weighted first order clauses. Many of the current state-of-the-art algorithms for learning MLNs have focused on relatively small datasets with few descriptive attributes, where predicates are mostly binary and the main task is usually prediction of links between entities. This paper addresses what is in a sense a complementary problem: learning the structure of an MLN that models the distribution of discrete descriptive attributes on medium to large datasets, given the links between entities in a relational database. Descriptive attributes are usually nonbinary and can be very informative, but they increase the search space of possible candidate clauses. We present an efficient new algorithm for learning a directed relational model (parametrized Bayes net), which produces an MLN structure via a standard moralization procedure for converting directed models to undirected models. Learning MLN structure in this way is 200-1000 times faster and scores substantially higher in predictive accuracy than benchmark algorithms on three relational databases. Copyright
Subjects 1712 Software
1702 Cognitive Sciences
Q-Index Code E1
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, 02:05:06 EST by Hassan Khosravi on behalf of Learning and Research Services (UQ Library)