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

Graph integration of structured, semistructured and unstructured data for data journalism