Document Open Access Logo

Linked Open Data Vocabularies for Semantically Annotated Repositories of Data Quality Measures (Short Paper)

Author Franz-Benjamin Mocnik

PDF

File

Thumbnail PDF
PDF
LIPIcs.GISCIENCE.2018.50.pdf
  • Filesize: 395 kB
  • 7 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • data quality
  • measure
  • semantics
  • Linked Open Data (LOD)
  • vocabulary
  • repository
  • reproducibility
  • OpenStreetMap (OSM)

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Abstract

The fitness for purpose concerns many different aspects of data quality. These aspects are usually assessed independently by different data quality measures. However, for the assessment of the fitness for purpose, a holistic understanding of these aspects is needed. In this paper we discuss two Linked Open Data vocabularies for formally describing measures and their relations. These vocabularies can be used to semantically annotate repositories of data quality measures, which accordingly adhere to common standards even if being distributed on multiple servers. This allows for a better understanding of how data quality measures relate and mutually constrain. As a result, it becomes possible to improve intrinsic data quality measures by evaluating their effectivity and by combining them.

Cite As Get BibTex

Franz-Benjamin Mocnik. Linked Open Data Vocabularies for Semantically Annotated Repositories of Data Quality Measures (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 50:1-50:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.50

Author Details

Franz-Benjamin Mocnik
  • Heidelberg University, Institute of Geography, Im Neuenheimer Feld 348, 69120 Heidelberg, Germany

Funding

This work was supported by the DFG project A framework for measuring the fitness for purpose of OpenStreetMap data based on intrinsic quality indicators (FA 1189/3-1).

Supplementary Materials

References

  1. Christopher Barron, Pascal Neis, and Alexander Zipf. A comprehensive framework for intrinsic OpenStreetMap quality analysis. Transactions in GIS, 18(6):877–895, 2014. Google Scholar
  2. Nicholas R. Chrisman. The role of quality information in the long-term functioning of a geographic information system. Cartographica, 21(2):79–87, 1984. Google Scholar
  3. Helen Couclelis. The certainty of uncertainty: GIS and the limits of geographic knowledge. Transactions in GIS, 7(2):165–175, 2003. Google Scholar
  4. Rudolphe Devillers, Yvan Bédard, and Roberg Jeansoulin. Multidimensional management of geospatial data quality information for its dynamic use within GIS. Photogrammetric Engineering and Remote Sensing, 71(2):205–215, 2005. Google Scholar
  5. Andrew U. Frank. Metamodels for data quality description. In Robert Jeansoulin and Michael F. Goodchild, editors, Data quality in geographic information. From error to uncertainty, page 15–29. Hermès, Paris, 1998. Google Scholar
  6. International Organization for Standardization. ISO 19157:2013. Geographic information. Data quality, 2013. Google Scholar
  7. Franz-Benjamin Mocnik. A scale-invariant spatial graph model. PhD thesis, Vienna University of Technology, 2015. Google Scholar
  8. Franz-Benjamin Mocnik. A novel identifier scheme for the ISEA Aperture 3 Hexagon Discrete Global Grid System. Cartography and Geographic Information Science, 2018. Google Scholar
  9. Franz-Benjamin Mocnik and Andrew U. Frank. Modelling spatial structures. Proceedings of the 12th Conference on Spatial Information Theory (COSIT), page 44–64, 2015. Google Scholar
  10. Franz-Benjamin Mocnik, Amin Mobasheri, Luisa Griesbaum, Melanie Eckle, Clemens Jacobs, and Carolin Klonner. A grounding-based ontology of data quality measures. Journal of Spatial Information Science, 16, 2018. Google Scholar
  11. Franz-Benjamin Mocnik, Alexander Zipf, and Hongchao Fan. The inevitability of calibration in VGI quality assessment. Proceedings of the 4th Workshop on Volunteered Geographic Information: Integration, Analysis, and Applications (VGI-Analytics), 2017. Google Scholar
  12. Karl Popper. The logic of scientific discovery. Routledge, London, 1992. Google Scholar
  13. Hansi Senaratne, Amin Mobasheri, Ahmed Loai Ali, Cristina Capineri, and Mordechai Haklay. A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science, 31(1):139–167, 2017. Google Scholar
  14. Yair Wand and Richard Y. Wang. Anchoring data quality dimensions in ontological foundations. Communications of the ACM, 39(11):86–95, 1996. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact