  Nowadays, journalism is facilitated by the existence of large amounts of
digital data sources, including many Open Data ones. Such data sources are
extremely heterogeneous, ranging from highly struc-tured (relational
databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text.
Journalists (and other classes of users lacking advanced IT expertise, such as
most non-governmental-organizations, or small public administrations) need to
be able to make sense of such heterogeneous corpora, even if they lack the
ability to de ne and deploy custom extract-transform-load work ows. These are
di cult to set up not only for arbitrary heterogeneous inputs , but also given
that users may want to add (or remove) datasets to (from) the corpus. We
describe a complete approach for integrating dynamic sets of heterogeneous data
sources along the lines described above: the challenges we faced to make such
graphs useful, allow their integration to scale, and the solutions we proposed
for these problems. Our approach is implemented within the ConnectionLens
system; we validate it through a set of experiments.
