(Big) Knowledge Graphs
In the last years, Big Data as well as Linked Data technologies gained wide attention. The primary goal of Big Data technologies is the high-performance analysis of large-scale data (volume and velocity), while Linked Data aims at integrating distributed, heterogeneous data (Variety). In this talk, we introduce the concept of knowledge graphs based on the RDF and Linked Data paradigm and discuss some crucial research and technology challenges including extraction, linking, quality assurance, authoring and visualization. We discuss some strategies, how the Big Data and Linked Data paradigms can be synergistically combined. We look at existing and promising future Big Knowledge Graph applications in the Digital Humanities/Cultural Heritage, Enterprise Data and Internet of Thing/Industry 4.0 domains.
Sören Auer studied Mathematics and Computer Science in Dresden, Hagen and Yekaterinburg (Russia). In 2006 he obtained his doctorate in Computer Science from Universität Leipzig. From 2006-2008 he worked with the database research group at the University of Pennsylvania, USA. In 2008 he founded AKSW research group at Leipzig University, which he led till 2013. Currently, he holds the chair for Enterprise Information Systems at University of Bonn and leads a department at Fraunhofer Institute for Analysis and Information Systems (IAIS). Sören’s research interests include semantic web technologies, knowledge engineering, software engineering, usability, as well as databases and information systems. He led / is leading several large-scale collaborative research projects such as the European Union’s FP7-ICT flagship project LOD2 comprising 15 partners from 11 countries. Sören is co-founder of several high-impact research and community projects such as the Wikipedia semantification project DBpedia, the OpenCourseWare authoring platform SlideWiki.org (received the OpenCourseware Innovation award) or the spatial data integration platform LinkedGeoData.
Back to the Summer School 2016 overview