A grounding-based ontology of data quality measures

Franz-Benjamin Mocnik, Amin Mobasheri, Luisa Griesbaum, Melanie Eckle, Clemens Jacobs, Carolin Klonner

Research output: Contribution to journalArticleAcademicpeer-review

29 Citations (Scopus)
41 Downloads (Pure)


Data quality and fitness for purpose can be assessed by data quality measures. Existing ontologies of data quality dimensions reflect, among others, which aspects of data quality are assessed and the mechanisms that lead to poor data quality. An understanding of which source of information is used to judge about data quality and fitness for purpose is, however, lacking. This article introduces an ontology of data quality measures by their grounding, that is, the source of information to which the data is compared to in order to assess their quality. The ontology is exemplified with several examples of volunteered geographic information (VGI), while also applying to other geographical data and data in general. An evaluation of the ontology in the context of data quality measures for OpenStreetMap (OSM) data, a well-known example of VGI, provides insights about which types of quality measures for OSM data have and which have not yet been considered in literature.
Original languageEnglish
Pages (from-to)1-25
Number of pages25
JournalJournal of Spatial Information Science
Issue number16
Publication statusPublished - 2018
Externally publishedYes


  • Data quality
  • Fitness for purpose
  • Data quality measure
  • Grounding
  • Ontology
  • Geographical data
  • OpenStreetMap
  • OSM
  • Volunteered geographic information
  • VGI


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