Join Bayes Nets: a new model class for relational data
By Oliver Schulte, Simon Fraser University
Many databases store data in relational format, with different types of entities and information about links between the entities. The field of statistical-relational learning has developed a number of new statistical models for such data, e.g. Probabilistic Relational Models and Markov Logic Networks. Instead of introducing a new model class, we propose using a standard model class in a new way: Join Bayes nets contain nodes that correspond to the descriptive attributes of the database tables, plus Boolean relationship nodes that indicate the presence of a link. As Join Bayes nets are just a special type of Bayes net, their semantics is standard (edges denote direct associations, d-separation implies probabilistic independence etc.), and Bayes net inference algorithms can be used "as is" to answer probabilistic queries involving relations. We discuss how Join Bayes Nets model various well-known statistical-relational phenomena like autocorrelation, aggregation and recursion.
This work is joint with Martin Ester, Hassan Khosravi, and Flavia Moser.
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