Probabilistic relational models extend Bayesian networks with the concepts of objects, their properties, and relations between them. In a way, they are to Bayesian networks as relational logic is to propositional logic.
A PRM specifies a template for a probability distribution over a database. The template includes a relational component that describes the relational schema for our domain, and a probabilistic component that describes the probabilistic dependencies that hold in our domain. A PRM has a coherent formal semantics in terms of probability distributions over sets of relational logic interpretations. Given a set of ground objects, a PRM specifies a probability distribution over a set of interpretations involving these objects (and perhaps other objects as well).
A PRM, together with a particular database of objects and relations, defines a probability distribution over the attributes of the objects.
L. GETOOR, N. FRIEDMAN, D. KOLLER, A. PFEFFER AND B. TASKAR
HTTPS://AI.STANFORD.EDU/~KOLLER/PAPERS/GETOOR+AL:SRL07.PDF
