The quality of linked data datasets being published has varying levels of quality. These datasets are created using mapping artifacts, which define the transformation rules from non-graph based data into graph based RDF data. Currently, quality issues are captured after the mapping artefact has been executed. Addressing quality issues within these artefacts will positively improve the quality of the resulting dataset. Furthermore, an explicit quality process for mappings will improve quality, maintenance, and reuse. The work presented includes an evaluation of the Mapping Quality Vocabulary (MQV) Framework, which aims to guide linked data producers in providing high quality data by enabling the quality assessment and subsequent improvement of the mapping artefacts. The evaluation of the framework consisted of 58 participants with varying level of background knowledge.
