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The Data Quality Vocabulary (DQV) provides a metadata model for expressing data quality. DQV was developed by the Data on the Web Best Practice (DWBP) Working Group of the World Wide Web Consortium (W3C) between 2013 and 2017. This paper aims at providing a deeper understanding of DQV. It introduces its key design principles, components, and the main discussion points that have been raised in the process of designing it. The paper compares DQV with previous quality documentation vocabularies and demonstrates the early uptake of DQV by collecting tools, papers, projects that have exploited and extended DQV.

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Linked data best practices are getting extremely popular: various companies and public institutions have started taking advantage of linked data principles for exposing their datasets, and for relating their datasets to those served by third parties. Such enthusiasm is due to the linked data promise of evolving into a Global Data Space. Linksets are sets of links relating datasets and they surely play a fundamental role in this promise. However, a stable and wellaccepted notion of linkset quality has not been yet defined. This paper contributes to overcome this lack by proposing a linkset quality measure. Among the different quality dimensions that can be addressed, the proposed measure focuses on completeness. The paper formally defines novel scoring functions and proposes an interpretation of these functions when maintaining and complementing third party datasets.