Advertisement

ADEQUATe: A Community-Driven Approach to Improve Open Data Quality

  • Lőrinc ThurnayEmail author
  • Thomas J. Lampoltshammer
  • Sebastian Neumaier
  • Tomáš Knap
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 339)

Abstract

This paper introduces the ADEQUATe project—a platform to improve the quality of open data in a community-driven fashion. First, the context of the project is discussed: the issue of quality of open data, its relevance in Austria and how ADEQUATe attempts to tackle these matters. Then the main components of the project are introduced, outlining how they support the goals of the project: Portal Watch managing monitoring, quality assessment and enhancement of data, the ADEQUATe Knowledge Base providing the backbone to the search and semantic enrichment components, the faceted Search functionality, Dataset profiles presenting an enriched overview of individual datasets to users, ADEQUATe’s GitLab instance providing the community dimension to the portal, and Odalic, a tool for semantic interpretation of tabular data. The paper is concluded with an outlook to the benefits of the project: easier data discovery, increased insight to data evolution, community engagement leading to contribution by a wider part of the population, increased transparency and democratization as well as positive feedback loops with data maintainers, public administration and the private sector.

Keywords

Community engagement Open data portal Open Governmental Data Semantic web Linked data 

Notes

Acknowledgement

The ADEQUATe project is funded by the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) under the program “ICT of the Future” (grant no. 849982) between October 2015 and June 2018.

References

  1. 1.
    Open Data Barometer: Open data barometer global report. World Wide Web Foundation (2017)Google Scholar
  2. 2.
    Council of European Union: Directive 2003/98/EC of the European Parliament and of the Council of 17 November 2003 on the re-use of public sector information. Off. J. 46, 90–96 (2003). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:32003L0098
  3. 3.
    Debattista, J., Dekkers, M., Guret, C., Lee, D., Mihindukulasooriya, N., Zaveri, A.: Data on the web best practices: data quality vocabulary. W3C Working Group Note (2016). https://www.w3.org/TR/2016/NOTE-vocab-dqv-20161215/
  4. 4.
    Dietrich, D., et al.: Open data handbook documentation, p. 11 (2012)Google Scholar
  5. 5.
    Höchtl, J., Schossböck, J., Lampoltshammer, T.J., Parycek, P.: The citizen scientist in the epolicy cycle. In: Ojo, A., Millard, J. (eds.) Government 3.0 – Next Generation Government Technology Infrastructure and Services: Roadmaps, Enabling Technologies & Challenges. Public Administration and Information Technology, vol. 32, pp. 37–61. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-63743-3_3CrossRefGoogle Scholar
  6. 6.
    Jetzek, T., Avital, M., Bjorn-Andersen, N.: Data-driven innovation through open government data. J. Theor. Appl. Electron. Commer. Res. 9(2), 100–120 (2014)CrossRefGoogle Scholar
  7. 7.
    Knap, T.: Towards Odalic, a semantic table interpretation tool in the ADEQUATe project. In: Proceedings of the 5th International Workshop on Linked Data for Information Extraction Co-located with the 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, 22 October 2017, pp. 26–37 (2017). http://ceur-ws.org/Vol-1946/paper-04.pdf
  8. 8.
    Knap, T., et al.: UnifiedViews: an ETL tool for RDF data management. Semant. Web J. (2018, to appear). http://www.semantic-web-journal.net/content/unifiedviews-etl-tool-rdf-data-management-0
  9. 9.
    Lampoltshammer, T.J., Scholz, J.: Citizen-driven geographic information science. In: Ceccaroni, L., Piera, J. (eds.) Analyzing the Role of Citizen Science in Modern Research, pp. 231–245. IGI Global, Hershey (2017)Google Scholar
  10. 10.
    Lampoltshammer, T.J., Scholz, J.: Open data as social capital in a digital society. In: Kapferer, E., Gstach, I., Koch, A., Sedmak, C. (eds.) Rethinking Social Capital: Global Contributions from Theory and Practice, pp. 137–150. Cambridge Scholars Publishing, Newcastle upon Tyne (2017)Google Scholar
  11. 11.
    Lóscio, B.F., et al.: Data on the web best practices. W3C Working Draft (2017). https://www.w3.org/TR/2017/REC-dwbp-20170131/#quality
  12. 12.
    Maali, F., Erickson, J., Archer, P.: Data catalog vocabulary (DCAT). W3C Recommendation (2014). https://www.w3.org/TR/2014/REC-vocab-dcat-20140116/
  13. 13.
    Neumaier, S., Umbrich, J., Polleres, A.: Automated quality assessment of metadata across open data portals. J. Data Inf. Qual. (JDIQ) 8(1), 2 (2016)Google Scholar
  14. 14.
    Neumaier, S., Umbrich, J., Polleres, A.: Lifting data portals to the web of data. In: WWW2017 Workshop on Linked Data on the Web (LDOW2017), Perth, Australia, 3–7 April 2017 (2017)Google Scholar
  15. 15.
    Shafranovich, Y.: Common Format and MIME Type for Comma-Separated Values (CSV) Files. RFC 4180, October 2005.  https://doi.org/10.17487/RFC4180, https://rfc-editor.org/rfc/rfc4180.txt
  16. 16.
    Tennison, J.: CSV on the Web: a primer. W3C Working Group Note (2016). https://www.w3.org/TR/2016/NOTE-tabular-data-primer-20160225/

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lőrinc Thurnay
    • 1
    Email author
  • Thomas J. Lampoltshammer
    • 1
  • Sebastian Neumaier
    • 2
  • Tomáš Knap
    • 3
  1. 1.Danube University KremsKrems an der DonauAustria
  2. 2.Vienna University of Economics and BusinessViennaAustria
  3. 3.Semantic Web CompanyViennaAustria

Personalised recommendations