Résumé
Due to the complexity of their data collection and their multi-scale approach, technical itineraries are complex and challenging to model. In this short abstract, we present a complete pipeline that aims to answer two kinds of expert competency questions: some by directly querying the data, and the others by learning a model able to reason with this complexity. By answering those, POND allows us to discover new applications and reverse engineer possible modifications.