Luzzu -- A Framework for Linked Data Quality Assessment

The increasing variety of Linked Data on the Web makes it challenging to determine the quality of this data, and subsequently to make this information explicit to data consumers. Despite the availability of a number of tools and frameworks to assess Linked Data Quality, the output of such tools is not suitable for machine consumption, and thus consumers can hardly compare and rank datasets in the order of fitness for use. This paper describes Luzzu, a framework for Linked Data Quality Assessment. Luzzu is based on four major components: (1) an extensible interface for defining new quality metrics, (2) an interoperable, ontology-driven back-end for representing quality metadata and quality problems that can be reused within different semantic frameworks, (3) a scalable stream processor for data dumps and SPARQL endpoints, and (4) a customisable ranking algorithm taking into account user-defined weights. We show that Luzzu scales linearly against the number of triples in a dataset. We also demonstrate the applicability of the Luzzu framework by evaluating and analysing a number of statistical datasets with regard to relevant metrics.