Thursday 25th March 2021 at 14:00 UTC (15:00 CET)
Semantic geographic knowledge on a world-scale – interlinking OpenStreetMap and knowledge graphs
Prof. Dr. Elena Demidova is Professor of Computer Science at the University of Bonn, Germany, where she leads Data Science & Intelligent Systems research group (DSIS). In the past, Elena worked as Research Group Leader at the L3S Research Center, Leibniz University of Hannover, Germany, and as Senior Research Fellow at the Web and Internet Science Group at the University of Southampton, UK. She received her PhD degree from the Leibniz University of Hannover in 2013 and her MSc degree in Information Engineering in 2006. Elena’s main research interests are Data Analytics, Open Data, the Web, and the Semantic Web.
OpenStreetMap (OSM) is a rich source of openly available volunteered geographic information on a world scale. However, representations of geographic entities in OSM are highly diverse and incomplete. Knowledge graphs (i.e. graph-based knowledge repositories) such as Wikidata, EventKG, and DBpedia are a rich source of contextual semantic information about geographic entities. For example, Wikidata contains over six million geographic entities, including locations, points of interest, mountain peaks, etc. Whereas knowledge graphs provide a wide range of complementary semantic information for geographic entities, interlinking between knowledge graphs and OSM is insufficient with the links mainly manually defined by volunteers. This lecture will introduce emerging approaches that address tighter integration of OSM and knowledge graphs; it gives particular attention to link discovery and semantic enrichment of OSM datasets.
How to participate: This event is an online event and is completely free-of-charge to attend. However, registration is necessary. A registration form is available here on Eventbrite. Registration closes on Wednesday 24 th March 2021 at 23:59 UTC.
More information: Event Chair Peter Mooney ( email@example.com ), Twitter @GISRUK Hashtag #GISRUKLectures