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

TL;DR

This paper presents a comprehensive system for integrating diverse data sources like structured, semi-structured, graph, and text data to support data journalism, enabling non-expert users to analyze heterogeneous datasets effectively.

Contribution

It introduces a complete approach and system, ConnectionLens, for scalable integration of heterogeneous data sources tailored for data journalism applications.

Findings

Successful implementation within the ConnectionLens system

Effective handling of dynamic and diverse data sources

Validated through experimental results

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.