Graph integration of structured, semistructured and unstructured data for data journalism
Oana Balalau, Concei\c{c}{\~a}o, Catarina, Héléna Galhardas, Ioana Manolescu, Tayeb Merabti, You, Jingmao, Youssr Youssef · arXiv (Cornell University) · 2020
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.