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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.

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.