Inducing probabilistic relational rules from probabilistic examples
Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, Mathias Verbeke · Lirias · 2015
We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be proba-bilistic. The setting is incorporated in the proba-bilistic rule learner ProbFOIL+, which combines principles of the rule learner FOIL with ProbLog, a probabilistic Prolog. We illustrate the approach by applying it to the knowledge base of NELL, the Never-Ending Language Learner.