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

  • André LangerEmail author
  • Valentin Siegert
  • Christoph Göpfert
  • Martin Gaedke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11153)

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

Linked data Open data Semantic web Data quality Quality assessment 

Notes

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.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Technische Universität ChemnitzChemnitzGermany

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