Course Abstract

Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, YAGO, Wikidata of Google Knowledge Graph. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This course tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue.


Learning Objectives

  • Understand problems of learning exception-enriched and numerical rules from highly biased and incomplete data

  • Discuss possible extensions of classical rule induction techniques to account for unstructured resources (e.g., text) along with the structured ones.


Instructor Bio:

Daria Stepanova is a research scientist at Bosch Center for Artificial Intelligence. Her research interests include Knowledge Representation and Reasoning with a special focus on the automatic acquisition of rules from structured knowledge. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on Semantic Data. Daria got her diploma degree in Applied Computer Science from the Department of Mathematics and Mechanics of St. Petersburg State University (Russia) in 2010 and a PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.

Daria Stepanova, PhD

Research Scientist | Bosch Center for AI

Background knowledge

  • This course is for current and aspiring Data Scientists and ML Engineers

  • Knowledge of following tools and concepts is useful:

  • Good understanding of machine learning topics

  • Familiarity with knowledge representation and reasoning is helpful, but not required