  Data journalism is the field of investigative journalism which focuses on
digital data by treating them as first-class citizens. Following the trends in
human activity, which leaves strong digital traces, data journalism becomes
increasingly important. However, as the number and the diversity of data
sources increase, heterogeneous data models with different structure, or even
no structure at all, need to be considered in query answering. Inspired by our
collaboration with Le Monde, a leading French newspaper, we designed a novel
query algorithm for exploiting such heterogeneous corpora through keyword
search. We model our underlying data as graphs and, given a set of search
terms, our algorithm nds links between them within and across the heterogeneous
datasets included in the graph. We draw inspiration from prior work on keyword
search in structured and unstructured data, which we extend with the data
heterogeneity dimension, which makes the keyword search problem computationally
harder. We implement our algorithm and we evaluate its performance using
synthetic and real-world datasets.
