Situation-Dependent Data Quality Analysis for Geospatial Data Using Semantic Technologies
BIS (Hrsg). Business Information Systems Workshops : BIS 2018 International Workshops, Berlin, Germany, July 18–20, 2018, Revised Papers. Cham: Springer International Publishing 2019 S. 566 - 578
Erscheinungsjahr: 2019
ISBN/ISSN: 978-3-030-04849-5
Publikationstyp: Buchbeitrag (Konferenzband)
Sprache: Englisch
Doi/URN: 10.1007/978-3-030-04849-5
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Inhaltszusammenfassung
In this paper we present a new way to evaluate geospatial data quality using Semantic technologies. In contrast to non-semantic approaches to evaluate data quality, Semantic technologies allow us to model situations in which geospatial data may be used and to apply costumized geospatial data quality models using reasoning algorithms on a broad scale. We explain how to model data quality using common vocabularies of ontologies in various contexts, apply data quality results using reasoning in ...In this paper we present a new way to evaluate geospatial data quality using Semantic technologies. In contrast to non-semantic approaches to evaluate data quality, Semantic technologies allow us to model situations in which geospatial data may be used and to apply costumized geospatial data quality models using reasoning algorithms on a broad scale. We explain how to model data quality using common vocabularies of ontologies in various contexts, apply data quality results using reasoning in a real-world application case using OpenStreetMap as our data source and highlight the results of our findings on the example of disaster management planning for rescue forces. We contribute to the Semantic Web community and the OpenStreetMap community by proposing a semantic framework to combine usecase dependent data quality assignments which can be used as reasoning rules and as data quality assurance tools for both communities respectively.» weiterlesen» einklappen
Klassifikation
DFG Fachgebiet:
Informatik
DDC Sachgruppe:
Ingenieurwissenschaften