University of Konstanz
Graduiertenkolleg / PhD Program
Computer and Information Science

Colloquium of the Department and the PhD Program


Statistical Relational Learning : an Inductive Logic Programming Approach, with some applications in bio-informatics


Prof. Dr. Luc De Raedt, University Freiburg
Freiburg, Germany

date & place

Wednesday, 10.05.2006, 16:15 h
Room C252


Probabilistic inductive logic programming (sometimes also called statistical relational learning) addresses one of the central questions of artificial intelligence: the integration of Probabilistic reasoning with first order Logic representations and machine Learning.

In this talk, I shall start from an inductive logic programming perspective and sketch how it can be extended with probabilistic methods. More specifically, I shall outline three settings for inductive logic programming: learning from entailment, learning from interpretations and learning from proofs or traces and show how they can be used to learn different types of probabilistic representations. The learning from entailment setting is natural when learning stochastic context free grammars and their upgrade, stochastic logic programs, the learning from interpretations settings is the method of choice when learning bayesian networks or bayesian logic programs, and learning from proofs or traces correspond to learning (hidden) markov models and their first order upgrades. The resulting settings will also be illustrated using various real-life examples from the field of bio-informatics.

This is joint work with Kristian Kersting and part of the EU project APRIL II (Application of Probabilistic Inductive Logic Programming II). The talk will be based on :
De Raedt, L., Kersting, K., Probabilistic Logic Learning, SIGKDD Explorations, Vol. 5(1), 2003.
De Raedt, L., Kersting, K. Probabilistic Inductive Logic Programming, In Proceedings ALT 2004, LNCS 3244, 2004.