
The two key ideas that come
to our rescue derive from the two approaches that we are trying to
combine. From relational logic, we
have the notion of universal patterns, which hold for all objects in a class. From Bayesian networks, we have the notion
of locality of interaction, which in the relational case has a particular
twist: Links give us precisely a
notion of “interaction”, and thereby provide a roadmap for which objects can
interact with each other.


In this example, we have a
template, like a universal quantifier for a probabilistic statement. It tells us: “For any registration record in my
database, the grade of the student in the course depends on the intelligence
of that student and the difficulty of that course.” This dependency will be instantiated for
every object (of the right type) in our domain. It is also associated with a conditional
probability distribution that specifies the nature of that dependence. We can also have dependencies over several
links, e.g., the satisfaction of a student on the teaching ability of the
professor who teaches the course.
