Linked Data datasets when they are published typically have varying levels of quality. These datasets are created using mapping artefacts, which define the transformation rules from non-graph based data into graph based RDF data. Currently, quality issues are detected after the mapping artefact has been executed and the Linked Data has already been published. It is argued in this paper that addressing quality issues within the mapping artefacts will positively improve the quality of the resulting dataset that is generated. Furthermore, we suggest that an explicit quality process for mappings will improve quality, maintenance, and reuse. This paper describes the evaluation of the Mapping Quality Vocabulary (MQV) Framework, which aims to guide linked data producers in producing high quality datasets, by enabling the quality assessment and subsequent improvement of the mapping artefacts. The evaluation of the MQV framework consisted of 58 participants with varying level of background knowledge.

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