An Evaluation of Metadata and Data Quality on Person-Level, Aggregated, Thesauri, Statistical Classifications, and Rectangular Data Sets

From 2012 to 2015 together with other Linked Data community members and experts from the social, behavioural, and economic sciences (SBE ), we developed diverse vocabularies to represent SBE metadata and rectangular data in RDF. The DDI-RDF Discovery Vocabulary (Disco) is designed to support the dissemination, management, and reuse of person-level data, i.e., data about individuals, households, and businesses, collected in form of responses to studies and archived for research purposes. The RDF Data Cube Vocabulary (Data Cube) is a W3C recommendation for expressing data cubes, i.e. multi-dimensional aggregate data. Physical Data Description (PHDD) is a vocabulary to model data in rectangular format. The data could either be represented in records with character-separated values (CSV ) or fixed length. The Simple Knowledge Organization System (SKOS) is a vocabulary to build knowledge organization systems such as thesauri, classification schemes, and taxonomies. XKOS is a SKOS extension to describe formal statistical classifications. To ensure high quality of and trust in both metadata and data, their representation in RDF must satisfy certain criteria specified in terms of RDF constraints. In this paper, we evaluated the metadata and data quality of large real world aggregated (QB), person-level (Disco), thesauri (SKOS), rectangular (PHDD), and statistical classification (XKOS) data sets by means of RDF constraints. RDF Constraints are instances of RDF constraint types either corresponding to RDF validation requirements or to data model specific constraint types. We validated more than 4.2 billion triples and 15 thousand data sets using the RDF Validator, a validation environment which is available at http://purl.org/net/rdfval-demo.