---
title: "Inducing Probabilistic Relational Rules from Probabilistic Examples"
authors:
  - "Luc De Raedt"
  - "Anton Dries"
  - "Ingo Thon"
  - "Guy Van den Broeck"
  - "Mathias Verbeke"
doi: ""
url: "https://ijcai.org/proceedings/2015/261"
pdf: "https://ijcai.org/proceedings/2015/0261.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "1835-1842"
track: "Main Track"
keywords:
---

# Inducing Probabilistic Relational Rules from Probabilistic Examples

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

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 1835-1842.

## 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.