Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue. We put a particular emphasis on the problems of learning exception-enriched rules from highly biased and incomplete data. Finally, we discuss possible extensions of classical rule induction techniques to account for unstructured resources (e.g., text) along with the structured ones.

Biography

Daria Stepanova is a research scientist at Bosch Center for Artificial Intelligence. Her research interests include Logic Programming, Description Logics, Inconsistency Management as well as Data Mining and Machine Learning. 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 under the supervision of Prof. Thomas Eiter. 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 and in a Russian company eKassir, which deals with the development of payment systems.