Graph integration of structured, semistructured and unstructured data for data journalism. Issue 104 (February 2022) - Anadiotis, Angelos Christos; Balalau, Oana; Conceição, Catarina; Galhardas, Helena; Haddad, Mhd Yamen; Manolescu, Ioana; Merabti, Tayeb; You, Jingmao
Abstract: Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (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 define and deploy custom extract-transform-load workflows, especially for dynamically varying sets of data sources. We describe a complete approach for integrating dynamic sets of heterogeneous datasets 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. Highlights: We define novel integration graphs and we construct them from arbitrary datasets We build the graphs leveraging data integration, information extraction, and data management We propose a novel algorithm finding matches across heterogeneous data sources We implement our approach on text, CSV, JSON, XML, RDF, PDF and relational datasets We evaluate our approach using a set of use cases with real journalistic datasets