QUAL: A Provenance-Aware Quality Model

Chris Baillie, Peter Edwards, Edoardo Pignotti

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
10 Downloads (Pure)


In this article, we present a model for quality assessment over linked data. This model has been designed to align with emerging standards for provenance on the Web to enable agents to reason about data provenance when performing quality assessment. The model also enables quality assessment provenance to be represented, thus allowing agents to make decisions about reuse of existing assessments. We also discuss the development of an OWL ontology as part of a software framework to support reasoning about data quality and assessment reuse. Finally, we evaluate this framework using two real-world case studies derived from transport and invasive-species monitoring applications.
Original languageEnglish
Article number12
Number of pages22
JournalInternational Journal of Data & Information Quality
Issue number3
Publication statusPublished - Feb 2015

Bibliographical note

The research described here is supported by the award made by the RCUK Digital Economy program to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.


  • data quality
  • provenance
  • ontology


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