Learning Probabilistic Relational Models
N. Friedman, L. Getoor, D. Koller, and A. Pfeffer.
To appear, Sixteenth International
Conf. on Artificial Intelligence (IJCAI), 1999.
Postscript version (160K)
A large portion of real-world data is stored in commercial relational database
systems. In contrast, most statistical learning methods work only
with ``flat'' data representations. Thus, to apply these methods,
we are forced to convert our data into a flat form, thereby losing much
of the relational structure present in our database. This paper builds
on the recent work on probabilistic relational models (PRMs), and
describes how to learn them from databases. PRMs allow the properties of
an object to depend probabilistically both on other properties of that
object and on properties of related objects. Although PRMs
are significantly more expressive than standard models, such as Bayesian
networks, we show how to extend well-known statistical methods for learning
Bayesian networks to learn these models. We describe both parameter
estimation and structure learning --- the automatic induction
of the dependency structure in a model. Moreover, we show how the
learning procedure can exploit standard database retrieval techniques for
efficient learning from large datasets. We present experimental results
on both real and synthetic relational databases.
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