ML Anthology

Inducing Probabilistic Relational Rules from Probabilistic Examples

Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, Mathias Verbeke
IJCAI 2015 pp. 1835-1843
/ijcai/2015/raedt2015ijcai-inducing/

Abstract

We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be probabilistic. The setting is incorporated in the probabilistic 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.

PDF IJCAI Semantic Scholar

Cite

Text

De Raedt et al. "Inducing Probabilistic Relational Rules from Probabilistic Examples." International Joint Conference on Artificial Intelligence, 2015.

Markdown

[De Raedt et al. "Inducing Probabilistic Relational Rules from Probabilistic Examples." International Joint Conference on Artificial Intelligence, 2015.](https://mlanthology.org/ijcai/2015/raedt2015ijcai-inducing/)

BibTeX

@inproceedings{raedt2015ijcai-inducing,
  title     = {{Inducing Probabilistic Relational Rules from Probabilistic Examples}},
  author    = {De Raedt, Luc and Dries, Anton and Thon, Ingo and Van den Broeck, Guy and Verbeke, Mathias},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {1835-1843},
  url       = {https://mlanthology.org/ijcai/2015/raedt2015ijcai-inducing/}
}