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Social Sensing Techniques for Geographic Knowledge Graphs

Teaser:
Startdatum: 16.07.2019 - 16:15
Enddatum: 16.07.2019 - 18:00
Adresse: Cartesium | Rotunde
Organisator/Ansprechpartner: Prof. Christian Freksa,
Preis: 0€

Social sensing utilizes data traces actively or passively emitted by humans and their devices for a variety of downstream tasks in behavioral research, urban studies, geographic information retrieval, and knowledge engineering. Applied to the level of geographic feature types, social sensing techniques can help to uncover otherwise latent characteristics of places, and, thereby, contribute to combining top-down and bottom-up approaches to the representation of knowledge in so-called knowledge graphs, e.g., Linked Data. This talk will discuss some of these techniques, the need for spatially-explicit models, and novel downstream tasks such as geographic knowledge graph summarization.

Biografie

Krzysztof Janowicz is a (full) professor for Geographic Information Science and Geoinformatics at the Geography Department of the University of California, Santa Barbara, USA. He is the program chair of the Cognitive Science Program, associate director of the Center for Spatial Studies, one of two Editors-in-Chief of the Semantic Web journal, a Faculty Research Affiliate of the Center for Information Technology and Society, and the community leader of the 52° North semantics community. Finally, he is running the STKO Lab which investigates the role of space and time for knowledge organization. Before, he was an Assistant Professor at the GeoVISTA Center, Department of Geography at the Pennsylvania State University, USA. Before moving to the US, he was working as postdoctoral researcher at the Institute for Geoinformatics (ifgi), University of Münster in Germany for the international research training group on Semantic Integration of Geospatial Information and the Münster Semantic Interoperability Lab (MUSIL). Methodologically, his niche is the combination of theory-driven (e.g., semantics) and data-driven (e.g., data mining) techniques.