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Preprints, Working Papers, ...

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

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.
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Preprints, Working Papers, ...
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Contributor : Ioana Manolescu <>
Submitted on : Tuesday, February 23, 2021 - 6:27:07 PM
Last modification on : Thursday, March 18, 2021 - 5:38:12 PM


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  • HAL Id : hal-03150441, version 1



Angelos Christos Anadiotis, Oana Balalau, Catarina Conceicao, Helena Galhardas, Mhd Yamen Haddad, et al.. Graph integration of structured, semistructured and unstructured data for data journalism. 2021. ⟨hal-03150441⟩



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