Skip to main content
SpringerLink
Log in

Quality Metrics for Linked Open Data

  • Conference paper
  • First Online:
Database and Expert Systems Applications (Globe 2015, DEXA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9261))

Abstract

The vision of the Linked Open Data (LOD) initiative is to provide a model for publishing data and meaningfully interlinking such dispersed but related data. Despite the importance of data quality for the successful growth of the LOD, only limited attention has been focused on quality of data prior to their publication on the LOD. This paper focuses on the systematic assessment of the quality of datasets prior to publication on the LOD cloud. To this end, we identify important quality deficiencies that need to be avoided and/or resolved prior to the publication of a dataset. We then propose a set of metrics to measure and identify these quality deficiencies in a dataset. This way, we enable the assessment and identification of undesirable quality characteristics of a dataset through our proposed metrics.

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.

    http://linkeddata.informatik.hu-berlin.de/uridbg.

  2. 2.

    http://validator.linkeddata.org/vapour.

  3. 3.

    http://jena.sourceforge.net/Eyeball.

  4. 4.

    http://139.91.183.30:9090/RDF/VRP.

  5. 5.

    http://lodlaundromat.org/.

  6. 6.

    http://eis-bonn.github.io/Luzzu/.

  7. 7.

    http://www.neon-project.org.

  8. 8.

    https://bitbucket.org/behkamal/new-metrics-codes/src.

References

  1. Hogan, A., Harth, A., Passant, A., Decker, S., Polleres, A.: Weaving the pedantic web. In: 3rd International Workshop on Linked Data on the Web (2010)

    Google Scholar 

  2. Fürber, C., Hepp, M.: Using semantic web resources for data quality management. In: Cimiano, P., Pinto, H. (eds.) EKAW 2010. LNCS, vol. 6317, pp. 211–225. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  3. Behkamal, B., Kahani, M., Paydar, S., Dadkhah, M., Sekhavaty, E.: Publishing Persian linked data; challenges and lessons learned. In: 5th International Symposium on Telecommunications (IST), pp. 732–737. IEEE (2010)

    Google Scholar 

  4. Paydar, S., Kahani, M., Behkamal, B.: Publishing data of ferdowsi university of mashhad as linked data. In: Computational Intelligence and Software Engineering (2010)

    Google Scholar 

  5. Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Quality Assessment for Linked Data: A Survey. Accepted in Semantic Web Journal (2014). http://www.semantic-web-journal.net/content/quality-assessment-linked-data-survey

  6. Lei, Y., Nikolov, A., Uren, V., Motta, E.: Detecting quality problems in semantic metadata without the presence of a gold standard. In: 5th International EON Workshop at International Semantic Web Conference, pp. 51–60 (2007)

    Google Scholar 

  7. Bizer, C., Cyganiak, R.: Quality-driven information filtering using the WIQA policy framework. Web Semant.: Sci., Serv. Agents World Wide Web 7, 1–10 (2009)

    Article  Google Scholar 

  8. Brüggemann, S., Grüning, F.: Using ontologies providing domain knowledge for data quality management. In: Pellegrini, T., Auer, S., Tochtermann, K., Schaffert, S. (eds.) Networked Knowledge - Networked Media. SCI, vol. 221, pp. 187–203. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Naumann, F., Leser, U., Freytag, J.C.: Quality-driven integration of heterogeneous information systems. In: 25th International Conference on Very Large Data Bases (VLDB 1999), Edinburgh, Scotland, UK, pp. 447–458 (1999)

    Google Scholar 

  10. Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Commun. ACM 45, 211–218 (2002)

    Article  Google Scholar 

  11. ISO: ISO/IEC 25012- Software engineering - Software product Quality Requirements and Evaluation (SQuaRE). Data quality model (2008)

    Google Scholar 

  12. Peralta, V.: Data freshness and data accuracy: A state of the art. Instituto de Computacion, Facultad de Ingenieria, Universidad de la Republica (2006)

    Google Scholar 

  13. Eppler, M.J., Wittig, D.: Conceptualizing information quality: A review of information quality frameworks from the last ten years. In: 5th International Conference on Information Quality, pp. 83–96 (2000)

    Google Scholar 

  14. Behkamal, B., Kahani, M., Bagheri, E., Jeremic, Z.: A Metrics-Driven approach for quality Assessment of Linked open Data. J. Theoritical Appl. Electron. Commer. Res. 9, 64–79 (2014)

    Google Scholar 

  15. Bagheri, E., Gasevic, D.: Assessing the maintainability of software product line feature models using structural metrics. Softw. Qual. J. 19, 579–612 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran

    Behshid Behkamal & Mohsen Kahani

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada

    Ebrahim Bagheri

Authors
  1. Behshid Behkamal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohsen Kahani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ebrahim Bagheri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behshid Behkamal .

Editor information

Editors and Affiliations

  1. Hewlett-Packard Enterprise, Sunnyvale, California, USA

    Qiming Chen

  2. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  3. Blaise Pascal University, Aubiere, France

    Farouk Toumani

  4. University of Linz, Linz, Austria

    Roland Wagner

  5. Universidad Politécnica de Valencia, Valencia, Spain

    Hendrik Decker

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Behkamal, B., Kahani, M., Bagheri, E. (2015). Quality Metrics for Linked Open Data. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22849-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22848-8

  • Online ISBN: 978-3-319-22849-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation