Foto: Anna Logue

IE 689 Relational Learning

Participants will be introduced to a specific form of Machine Learning that aims at learning relational rules from relational data. They should understand the strengths and limitations of this type of machine learning methods in comparison to more widely used propositional learning approaches and gather practical experiences with using the methods on example data.

Introductory Lecture

Recommended Knowledge

Data Mining
First-Order Logic
Problem Solving as Search

Dates

Information regarding lecture and tutorial can be found in the university calendar (online Vorlesungsverzeichnis).

ILIAS and Registration

The course uses ILIAS. Any further information can be found there. You can register for the course via the portal.

Assessments

Written examination.
Admission requirement: Successful participation in the exercises (>50% on each homework assignment)

Instructors

Lecture: Prof. Dr. Heiner Stuckenschmidt / Dr. Christian Meilicke
Tutorial: Manuel Fink

Literature

Luc De Raedt. Logical and Relational Learning. Springer 2010. Chapters 1-6.
Galarraga et al.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases.Proceedings of the 22nd international conference on World Wide Web. Pages 413--422, ACM, 2013.
Meilicke et al.: Anytime bottom-up rule learning for knowledge graph completion. In Proceedings of the International Joint Conference on Artificial Intelligence, 2019.