Version Control and Change Validation for RDF Datasets

The dynamic and distributed nature of the Semantic Web demands for methodologies and systems fostering collective participation to the evolution of datasets. In collaborative and iterative processes for dataset development, it is important to keep track of individual changes for provenance. Different scenarios may require mechanisms to foster consensus, resolve conflicts between competing changes, reversing or ignoring changes etc. In this paper, we perform a landscape analysis of version control for RDF datasets, emphasizing the importance of change reversion to support validation. Firstly, we discuss different representations of changes in RDF datasets and introduce higher-level perspectives on change. Secondly, we analyze diverse approaches to version control. We conclude by focusing on validation, characterizing it as a separate need from the mere preservation of different versions of a dataset.