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.
Cite
@article{arxiv.2012.08830,
title = {Graph integration of structured, semistructured and unstructured data for data journalism},
author = {Angelos-Christos Anadiotis and Oana Balalau and Catarina Conceicao and Helena Galhardas and Mhd Yamen Haddad and Ioana Manolescu and Tayeb Merabti and Jingmao You},
journal= {arXiv preprint arXiv:2012.08830},
year = {2020}
}
Comments
40 pages, 9 figures. arXiv admin note: substantial text overlap with arXiv:2007.12488, arXiv:2009.04283