Learning graphical models for relational data via lattice search

Schulte, Oliver and Khosravi, Hassan (2012) Learning graphical models for relational data via lattice search. Machine Learning, 88 3: 331-368. doi:10.1007/s10994-012-5289-4


Author Schulte, Oliver
Khosravi, Hassan
Title Learning graphical models for relational data via lattice search
Journal name Machine Learning   Check publisher's open access policy
ISSN 0885-6125
1573-0565
Publication date 2012-09-01
Sub-type Article (original research)
DOI 10.1007/s10994-012-5289-4
Open Access Status Not yet assessed
Volume 88
Issue 3
Start page 331
End page 368
Total pages 38
Place of publication New York, United States
Publisher Springer
Language eng
Abstract Many machine learning applications that involve relational databases incorporate first-order logic and probability. Relational extensions of graphical models include Parametrized Bayes Net (Poole in IJCAI, pp. 985-991, 2003), Probabilistic RelationalModels (Getoor et al. in Introduction to statistical relational learning, pp. 129-173, 2007), and Markov Logic Networks (MLNs) (Domingos and Richardson in Introduction to statistical relational learning, 2007). 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 a graphical model that models the distribution of discrete descriptive attributes 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 Parametrized Bayes Net that performs a level-wise search through the table join lattice for relational dependencies. From the Bayes net we obtain an MLN structure via a standard moralization procedure for converting directed models to undirected models. Learning MLN structure by moralization is 200-1000 times faster and scores substantially higher in predictive accuracy than benchmark MLN algorithms on five relational databases.
Keyword Bayes nets
Graphical models
Markov logic networks
Statistical-relational 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
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 14 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 15 Sep 2016, 02:10:09 EST by Hassan Khosravi on behalf of Learning and Research Services (UQ Library)