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Employing Geospatial Semantics and Semantic WebTechnologies in Natural Disaster Management

: Hellmund, Tobias; Schenk, Manfred; Hertweck, Philipp; Moßgraber, Jürgen

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Alam, Mehwish (ed.):
SEMPDS 2019, Posters and Demos at SEMANTiCS 2019. Online resource : Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems co-located with 15th International Conference on Semantic Systems (SEMANTiCS 2019) Karlsruhe, Germany, September 9th to 12th, 2019
Karlsruhe: CEUR, 2019 (CEUR Workshop Proceedings 2451)
ISSN: 1613-0073
5 pp.
International Conference on Semantic Systems (SEMANTiCS) <15, 2019, Karlsruhe>
European Commission EC
H2020; 700475; beAWARE
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

In a natural disaster situation, it is crucial to orchestrate an efficient response, which prevents, or - at least - mitigates damages. Based on the assumption, that a well-informed decision maker can make the best decisions, s/he should have access to all available information. Thus, employing both internal and external data empowers decision makers. Since natural disasters are usually limited to a certain (previously unknown) area, it is of high importance to get to know about the local context of a disaster. Critical infrastructure, such as hospitals, energy supply, buildings with vulnerable beings (kindergarten, elder care, etc.) play an important role in crisis management. Nevertheless, a decision maker might not be aware of all of these places; yet, knowledge about these can often be found in external, public knowledge bases, such as Wikidata. Semantic Web Technology offers tools to integrate data from diverse data stores, offering a giant source of information. To improve situational awareness, this information should be tapped. By employing geospatial semantic features of knowledge bases, it is possible to integrate several data stores and only find information, that is valid within the range of a disaster and therefore of interest to a decision maker. The poster presents the integration of Wikidata as an external knowledge-base into a Decision-Support-System by using federated queries. Through employing geospatial semantic features, only relevant information is retrieved.