Guy Van den Broeck - Inducing Probabilistic Relational Rules from Probabilistic Examples (bibtex)

Inducing Probabilistic Relational Rules from Probabilistic Examples (bibtex)

by Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck and Mathias Verbeke

Abstract:

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.

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Reference:

Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck and Mathias Verbeke. Inducing Probabilistic Relational Rules from Probabilistic Examples, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015.

Bibtex Entry:

@inproceedings{VerbekeIJCAI15,
  author = {De Raedt, Luc and Dries, Anton and Thon, Ingo and Van den Broeck, Guy and Verbeke, Mathias},
  title = {Inducing Probabilistic Relational Rules from Probabilistic Examples},
  booktitle={Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI)},
  year = {2015},
  url={http://starai.cs.ucla.edu/papers/VerbekeIJCAI15.pdf},
  keywords   = {conference,selective}
}

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