A Preliminary Investigation of Reversing RML: From an RDF dataset to its Column-Based data source

A large percentage of scientific data with tabular structure are published on the Web of Data as interlinked RDF datasets. When we come to the issue of long-term preservation of such RDF-based digital objects, it is important to provide full support for reusing them in the future. In particular, it should include means for both players who have no familiarity with RDF data model and, at the same time, who by working only with the native format of the data still provide sufficient information. To achieve this, we need mechanisms to bring the data back to their original format and structure.

In this paper, we investigate how to perform the reverse process for column-based data sources. In particular, we devise an algorithm, RML2CSV, and exemplify its implementation in transforming an RDF dataset into its CSV tabular structure, through the use of the same RML mapping document that was used to generate the set of RDF triples. Through a set of content-based criteria, we attempt a comparative evaluation to measure the similarity between the rebuilt CSV and the original one. The results are promising and show that, under certain assumptions, RML2CSV reconstructs the same data with the same structure, offering more advanced digital preservation services.