This is a Keynote Lecture delivered at the Big Data Analytics Summer School 2020 by Prof. Georgios Paliouras, NCSR “Demokritos”, iASiS Project Coordinator.

The Lecture has been delivered at the Big Data Analytics Summer School 2020 by Dr. Sahar Vahdati, University of Oxford.

This is an Invited Lecture delivered at the Big Data Analytics Summer School 2020 by Dr. Debasis Das, Indian Institutes of Technology(IIT) Jodhpur.

This is a Lecture delivered at Big the Data Analytics Summer School 2020 by Dr. Tomas Krajnik, Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague.

This is a Keynote Lecture delivered at the Big Data Analytics Summer School 2020 by Dr Gloria Bordogna, Italian National Research Council IREA

This is a Keynote Lecture delivered at the Big Data Analytics Summer School 2020 by Dr. Mariana Damova, Mozajka.

This is a Lecture delivered at the Big Data Analytics Summer School 2020 by Dr. Simon Scerri, Fraunhofer IAIS.

This is a Lecture delivered at the Big Data Analytics Summer School 2020 by Dr. Emanuel Sallinger, University of Oxford.

The increasing availability of scholarly metadata in the form of Knowledge Graphs (KG) offers opportunities for studying the structure of scholarly communication and the evolution of science. Such KGs build the foundation for knowledge-driven tasks e.g., link discovery, prediction and entity classification which allows to provide recommendation services. Knowledge graph embedding (KGE) models have been investigated for such knowledge-driven tasks in different application domains.

In this lecture, we introduce the aspect of reasoning in Knowledge Graphs. We give a broad overview focusing on the multitude of reasoning techniques: spanning logic-based reasoning, embedding-based reasoning, neural network-based reasoning, etc. In particular, we discuss three dimensions of reasoning in Knowledge Graphs. Complementing these dimensions, we will structure our exploration based on a pragmatic view of reasoning tasks and families of reasoning tasks: reasoning for knowledge integration, knowledge discovery and application services.