This article introduces an exploratory computational approach to extending the realm of automated journalism from simple descriptions to richer and more complex eventdriven narratives, based on original applied research in structured journalism. The practice of automated journalism is reviewed and a major constraint on the potential to automate journalistic writing is identified, namely the ab-sence of data models sufficient to encode the journalistic knowledge necessary for automatically writ-ing event-driven narratives. A detailed proposal addressing this constraint is presented, based on the representation of journalistic knowledge as structured event and structured narrative data. We de-scribe a prototyped database of structured events and narratives, and introduce two methods of using event and narrative data from the prototyped database to provide journalistic knowledge to a com-mercial automated writing platform. Detailed examples of the use of each method are provided, in-cluding a successful application of the approach to stories about car chases, from initial data reporting through to automatically generated text. A framework for evaluating automatically generated event-driven narratives is proposed, several technical and editorial challenges to applying the approach in practice are discussed, and several high-level conclusions about the importance of data structures in automated journalism workflows are provided.