SemQuire - a Linked Data Quality Assessment Tool
Authors: André Langer, Martin Gaedke
The World Wide Web represents a tremendous source of knowledge. Everybody can constantly create, publish and alter data, thus the amount of information from available data sources from nearly arbitrary knowledge domains constantly increases. Throughout the last years, Open Data initiatives and the Semantic Web community have emphasized the need to publish data in a structured format based on open standards and ideally linked to other data sources.
But that does not necessarily lead to error-free information and data of good quality. Several attempts have already been made to check the quality of particular data sets, however, most of the software tools are not available any longer or only address a limited application domain.
So, it would be of high relevance to create and apply a real-world software component that is capable of measuring the most relevant quality metrics in a generic fashion.
We therefore present SemQuire, a quality assessment tool for analyzing quality aspects of particular Linked Data sources both in the Open Data context as well as in the Enterprise Data Service context. It is based on open standards such as W3C's RDF, SPARQL and DQV, and implements as a proof-of-concept a basic set of 58 recommended intrinsic, representational, contextual and accessibility quality metrics.
You can find a SemQuire instance here for testing purposes.