LUXLOGAI 2018: LUXEMBOURG LOGIC FOR AI SUMMIT
RW SUMMER SCHOOL ON TUESDAY, SEPTEMBER 25TH, 2018
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08:30-12:00 Session 56
Location: MSA 4.530
08:30
Machine Learning with and for Knowledge Graphs

ABSTRACT. Large-scale cross-domain knowledge graphs, such as DBpedia or Wikidata, are some of the most popular and widely used datasets of the Semantic Web. In this paper, we introduce some of the most popular knowledge graphs on the Semantic Web. We discuss how machine learning is used to improve those knowledge graphs, and how they can be exploited as background knowledge in popular machine learning tasks, such as recommender systems.

10:00-10:30Coffee Break
13:30-14:30Lunch Break
14:30-16:00 Session 58: Group Presentations

Presentation of the results from the morning session's working groups.

Location: MSA 4.530
16:00-16:30Coffee Break
16:00-19:30 Session 59
Location: MSA 4.530
16:00
Rule Induction and Reasoning over Knowledge Graphs

ABSTRACT. 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.