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.