In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. In an analogy to probabilistic relational models (PRMs), we wish to represent the joint probability distribution over missing values in a database or logic program us ing a Bayesian network. This paper presents an extension of logic programs that makes it pos sible to specify a joint probability distribution over terms built from Skolem functors in the pro gram. Our extension is based on constraint logic programming (CLP), so we call the extended language CLP(BN). We show that CLP(BN) subsumes PRMs; this greater expressivity car ries both advantages and disadvantages for CLP(BN). We also show that algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP(BN) programs. An implementation of CLP(BN) is publicly available as part of YAP Prolog at
Santos REF presented an extension of logic programs that makes it possible to specify a joint probability distribution over missing values in a database or logic program, in analogy to PRMs.
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge
ArXiv
ArXiv
Computer Science