  This study leverages narrative from global newspapers to construct
theme-based knowledge graphs about world events, demonstrating that features
extracted from such graphs improve forecasts of industrial production in three
large economies compared to a number of benchmarks. Our analysis relies on a
filtering methodology that extracts "backbones" of statistically significant
edges from large graph data sets. We find that changes in the eigenvector
centrality of nodes in such backbones capture shifts in relative importance
between different themes significantly better than graph similarity measures.
We supplement our results with an interpretability analysis, showing that the
theme categories "disease" and "economic" have the strongest predictive power
during the time period that we consider. Our work serves as a blueprint for the
construction of parsimonious - yet informative - theme-based knowledge graphs
to monitor in real time the evolution of relevant phenomena in socio-economic
systems.
