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

Abstract : 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.
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https://hal.inria.fr/hal-02904797
Contributor : Ioana Manolescu <>
Submitted on : Thursday, October 29, 2020 - 11:00:47 PM
Last modification on : Saturday, October 31, 2020 - 3:30:58 AM

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  • HAL Id : hal-02904797, version 2
  • ARXIV : 2007.12488

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Oana Balalau, Catarina Conceição, Helena Galhardas, Ioana Manolescu, Tayeb Merabti, et al.. Graph integration of structured, semistructured and unstructured data for data journalism. 36ème Conférence sur la Gestion de Données – Principes, Technologies et Applications (informal publication only), 2020. ⟨hal-02904797v2⟩

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