Abstract
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. The CLP(\(\cal{BN}\)) language represents the joint probability distribution over missing values in a database or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP(\(\cal{BN}\)) programs. An implementation of CLP(\(\cal{BN}\)) is publicly available as part of YAP Prolog at http://www.ncc.up.pt/~vsc/Yap .
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Santos Costa, V., Page, D., Cussens, J. (2008). CLP(\(\cal{BN}\)): Constraint Logic Programming for Probabilistic Knowledge. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds) Probabilistic Inductive Logic Programming. Lecture Notes in Computer Science(), vol 4911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78652-8_6
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