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Journal Articles Information Systems Year : 2022

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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Dates and versions

hal-03150441 , version 1 (23-02-2021)
hal-03150441 , version 2 (08-09-2021)

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Cite

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. Information Systems, 2022, 104, pp.101846. ⟨10.1016/j.is.2021.101846⟩. ⟨hal-03150441v2⟩
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