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2019 | OriginalPaper | Chapter

Situation-Dependent Data Quality Analysis for Geospatial Data Using Semantic Technologies

Authors : Timo Homburg, Frank Boochs

Published in: Business Information Systems Workshops

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter ADEQUATe: A Community-Driven Approach to Improve Open Data Quality
next chapter Indicating Studies’ Quality Based on Open Data in Digital Libraries
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Metadata
Title
Situation-Dependent Data Quality Analysis for Geospatial Data Using Semantic Technologies
Authors
Timo Homburg
Frank Boochs
Copyright Year
2019
Book
Business Information Systems Workshops

Print ISBN: 978-3-030-04848-8

Electronic ISBN: 978-3-030-04849-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-04849-5_49

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