Skip to main content
SpringerLink
Log in

SemQuire - Assessing the Data Quality of Linked Open Data Sources Based on DQV

  • Conference paper
  • First Online:
Book cover Current Trends in Web Engineering (ICWE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11153))

Included in the following conference series:

Abstract

The World Wide Web represents a tremendous source of knowledge, whose amount constantly increases. Open Data initiatives and the Semantic Web community have emphasized the need to publish data in a structured format based on open standards and ideally linked to other data sources. But that does not necessarily lead to error-free information and data of good quality. It would be of high relevance to have a software component that is capable of measuring the most relevant quality metrics in a generic fashion as well as rating these results.

We therefore present SemQuire, a quality assessment tool for analyzing quality aspects of particular Linked Data sources both in the Open Data context as well as in the Enterprise Data Service context. It is based on open standards such as W3C’s RDF, SPARQL and DQV, and implements as a proof-of-concept a basic set of 55 recommended intrinsic, representational, contextual and accessibility quality metrics. We provide a use case for evaluating SemQuire’s feasibility and effectiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.w3.org/TR/vocab-dqv/.

  2. 2.

    http://iso25000.com/index.php/en/iso-25000-standards/iso-25012.

  3. 3.

    https://www.w3.org/TR/vocab-dqv/ #DimentsionsMetricsHints.

  4. 4.

    https://dbpedia.org.

  5. 5.

    https://www.w3.org/wiki/ConverterToRdf#Frameworks.

  6. 6.

    http://dbpedia.org/.

  7. 7.

    http://wikidata.org/.

  8. 8.

    http://linkedmdb.org/.

  9. 9.

    https://www.w3.org/TR/vocab-dqv/.

References

  1. Assaf, A., Troncy, R., Senart, A.: Roomba: an extensible framework to validate and build dataset profiles. In: Gandon, F., Guéret, C., Villata, S., Breslin, J., Faron-Zucker, C., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9341, pp. 325–339. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25639-9_46

    Chapter  Google Scholar 

  2. Debattista, J., Lange, C., Auer, S.: daQ, an ontology for dataset quality information. In: CEUR Workshop Proceedings, vol. 1184 (2014)

    Google Scholar 

  3. Flemming, A.: Qualitätsmerkmale von Linked Data-veröffentlichenden Datenquellen, pp. 1–174 (2011). http://www.dbis.informatik.hu-berlin.de/fileadmin/research/papers/diploma_seminar_thesis/Diplomarbeit_Annika_Flemming.pdf

  4. Fürber, C., Hepp, M.: Towards a vocabulary for data quality management in semantic web architectures. Proceedings of the 1st International Workshop on Linked Web Data Management - LWDM 2011, p. 1 (2011)

    Google Scholar 

  5. Hogan, A., Harth, A., Passant, A., Decker, S., Polleres, A.: Weaving the pedantic web. In: CEUR Workshop Proceedings, vol. 628 (2010)

    Google Scholar 

  6. Hogan, A., Umbrich, J., Harth, A., et al.: An empirical survey of linked data conformance. Web Semant. 14, 14–44 (2012)

    Article  Google Scholar 

  7. Langer, A., Gaedke, M.: Fame.q -a formal approach to master quality in enterprise linked data. In: Proceedings of the 15th International Conference WWW/Internet (ICWI2016), pp. 51–58. IADIS, October 2016

    Google Scholar 

  8. Langer, A., Gaedke, M.: DaQAR - an ontology for the uniform exchange of comparable linked data quality assessment requirements. In: Mikkonen, T., Klamma, R., Hernández, J. (eds.) ICWE 2018. LNCS, vol. 10845, pp. 234–242. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91662-0_18

    Chapter  Google Scholar 

  9. Mendes, P.N., Mühleisen, H., Bizer, C.: Sieve: linked data quality assessment and fusion. In: Proceedings of the 2012 Joint EDBT/ICDT Workshops, EDBT-ICDT 2012, pp. 116–123. ACM, New York (2012)

    Google Scholar 

  10. Redman, T.C.: Data Quality: The Field Guide. Digital Press, Newton (2001)

    Google Scholar 

  11. Ruan, T., Dong, X., Li, Y., Wang, H.: KBMetrics A Multi-purpose Tool for Measuring the Quality of Linked Open Data Sets (2015)

    Google Scholar 

  12. Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manage. Inf. Syst. 12(4), 5–33 (1996)

    Article  Google Scholar 

  13. Zaveri, A., Rula, A., Maurino, A., et al.: Quality assessment for linked open data: a survey. Semant. Web J. 1, 1–31 (2014)

    Google Scholar 

Download references

Acknowledgment

This work was supported by the grant from the German Federal Ministry of Education and Research (BMBF) for the LEDS Project under grant agreement No 03WKCG11D.

Author information

Authors and Affiliations

  1. Technische Universität Chemnitz, Chemnitz, Germany

    André Langer, Valentin Siegert, Christoph Göpfert & Martin Gaedke

Authors
  1. André Langer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Valentin Siegert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christoph Göpfert
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martin Gaedke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to André Langer .

Editor information

Editors and Affiliations

  1. Faculty of Informatics, University of Lugano, Lugano, Switzerland

    Cesare Pautasso

  2. Quercus Software Engineering Group, Universidad de Extremadura, Cáceres, Spain

    Fernando Sánchez-Figueroa

  3. Tampere University of Technology, Tampere, Finland

    Kari Systä

  4. Escuela Politécnica, Universidad de Extremadura, Cáceres, Spain

    Juan Manuel Murillo Rodríguez

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Langer, A., Siegert, V., Göpfert, C., Gaedke, M. (2018). SemQuire - Assessing the Data Quality of Linked Open Data Sources Based on DQV. In: Pautasso, C., Sánchez-Figueroa, F., Systä, K., Murillo Rodríguez, J. (eds) Current Trends in Web Engineering. ICWE 2018. Lecture Notes in Computer Science(), vol 11153. Springer, Cham. https://doi.org/10.1007/978-3-030-03056-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03056-8_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03055-1

  • Online ISBN: 978-3-030-03056-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation