What is it about?

DQV deals with the quest to exchange quality information. DQV relates to different types of quality statements, which include Quality Annotations, Standards, Quality Policies, Quality Measurements, and Quality Provenance. Quality information pertains to one or more quality characteristics relevant to the consumer (aka, Quality Dimensions). Implementers can adopt their own quality definitions and dimensions.

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Why is it important?

DQV is a (meta)data model implemented as an RDF vocabulary, whose original motivation is the documentation of the quality of DCAT Datasets and Distributions. DQV is a community effort developed in the W3C DWBP Working Group, which gives it high visibility and status. In addition, and more than other proposals for expressing quality information, it specifically embraces design principles meant to favor its reusability and uptake. The adoption of minimal ontological commitment has led us to avoid unnecessary domain restrictions, for DQV can be applied to any web resource, not only DCAT Datasets and Distributions.

Perspectives

The Data Quality Vocabulary is the outcome of a large amount of group work and community feedback, as part of the W3C process. And it follows indeed the best practices of the Linked Data community for designing and re-using data vocabularies (ontologies). This is important for me from a research perspective, but also for a practical one, since my organization (Europeana.eu) has since then implemented it to share its own quality measurements for the metadata we harvest for digital cultural heritage objects throughout Europe!

Antoine Isaac

Read the Original

This page is a summary of: Introducing the Data Quality Vocabulary (DQV), Semantic Web, November 2020, IOS Press,
DOI: 10.3233/sw-200382.
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