Linked Thesauri Quality Assessment and Documentation for Big Data Discovery

Abstract

Thesauri are knowledge systems which may ease Big Data access, fostering their integration and re-use. Currently several Linked Data thesauri covering multi-disciplines are available. They provide a semantic foundation to effectively support cross-organization and cross-disciplinary management and usage of Big Data. Thesauri effectiveness is affected by their quality. Diverse quality measures are available taking into account different facets. However, an overall measure is needed to compare several thesauri and to identify those more qualified for a proper reuse. In this paper, we propose a Multi Criteria Decision Making based methodology for the documentation of the quality assessment of linked thesauri as a whole. We present a proof of concept of the Analytic Hierarchy Process adoption to the set of Linked Data thesauri for the Environment deployed in LusTRE. We discuss the step-by-step practice to document the overall quality measurements, generated by the quality assessment, with the W3C promoted Data Quality Vocabulary.