LOP: capturing and linking open provenance on LOD cycle

Abstract

The Web of Data has emerged as a means to expose, share, reuse, and connect information on the Web identified by URIs using RDF as a data model, following Linked Data Principles. However, the reuse of third party data can be compromised without proper data quality assessments. In this context, important questions emerge: how can one trust on published data and links? Which manipulation, modification and integration operations have been applied to the data before its publication? What is the nature of comparisons or transformations applied to data during the interlinking process? In this scenario, provenance becomes a fundamental element. In this paper, we describe an approach for generating and capturing Linked Open Provenance (LOP) to support data quality and trustworthiness assessments, which covers preparation and format transformation of traditional data sources, up to dataset publication and interlinking. The proposed architecture takes advantage of provenance agents, orchestrated by an ETL workflow approach, to collect provenance at any specified level and also link it with its corresponding data. We also describe a real use case scenario where the architecture was implemented to evaluate the proposal.