Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the rules have been expressed as logical statements and also the examples and their classification have been purely logical. We upgrade rule learning to a probabilistic setting, in which both the examples themselves as well as their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL, which combines the principles of the relational rule learner FOIL with the probabilistic Prolog, ProbLog. We report also on some experiments that demonstrate the utility of the approach.
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Department of Computer Science, Katholieke Universiteit Leuven, Belgium
Luc De Raedt & Ingo Thon
Dipartimento di Sistemi e Informatica, Universitá degli Studi di Firenze, Italy
Paolo Frasconi
Dipartimento di Informatica, Università degli Studi di Bari, Via E. Orabona 4, 70125, Bari, Italy
Francesca A. Lisi
© 2011 Springer-Verlag Berlin Heidelberg
About this paper Cite this paperDe Raedt, L., Thon, I. (2011). Probabilistic Rule Learning. In: Frasconi, P., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2010. Lecture Notes in Computer Science(), vol 6489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21295-6_9
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Publisher Name: Springer, Berlin, Heidelberg
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