  The dynamic nature of Web data gives rise to a multitude of problems related
to the identification, computation and management of the evolving versions and
the related changes. In this paper, we consider the problem of change
recognition in RDF datasets, i.e., the problem of identifying, and when
possible give semantics to, the changes that led from one version of an RDF
dataset to another. Despite our RDF focus, our approach is sufficiently general
to engulf different data models that can be encoded in RDF, such as relational
or multi-dimensional. In fact, we propose a flexible, extendible and
data-model-independent methodology of defining changes that can capture the
peculiarities and needs of different data models and applications, while being
formally robust due to the satisfaction of the properties of completeness and
unambiguity. Further, we propose an ontology of changes for storing the
detected changes that allows automated processing and analysis of changes,
cross-snapshot queries (spanning across different versions), as well as queries
involving both changes and data. To detect changes and populate said ontology,
we propose a customizable detection algorithm, which is applicable to different
data models and applications requiring the detection of custom, user-defined
changes. Finally, we provide a proof-of-concept application and evaluation of
our framework for different data models.
