| Abstract | A large portion of real-world data is stored in com-mercial 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 mod-
els (PRMs), and describes how to learn them from
databases. PRMs allow the properties of an object
to depend probabilistically both on other proper-
ties of that object and on properties of related ob-
jects. Although PRMs are significantly more ex-
pressive than standard models, such as Bayesian
networks, we show how to extend well-known sta-
tistical 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 re-
lational databases.
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