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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
References
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
Debattista, J., Lange, C., Auer, S.: daQ, an ontology for dataset quality information. In: CEUR Workshop Proceedings, vol. 1184 (2014)
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
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)
Hogan, A., Harth, A., Passant, A., Decker, S., Polleres, A.: Weaving the pedantic web. In: CEUR Workshop Proceedings, vol. 628 (2010)
Hogan, A., Umbrich, J., Harth, A., et al.: An empirical survey of linked data conformance. Web Semant. 14, 14–44 (2012)
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
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
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)
Redman, T.C.: Data Quality: The Field Guide. Digital Press, Newton (2001)
Ruan, T., Dong, X., Li, Y., Wang, H.: KBMetrics A Multi-purpose Tool for Measuring the Quality of Linked Open Data Sets (2015)
Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manage. Inf. Syst. 12(4), 5–33 (1996)
Zaveri, A., Rula, A., Maurino, A., et al.: Quality assessment for linked open data: a survey. Semant. Web J. 1, 1–31 (2014)
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
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)