The reconstruction of geographical data over a century, allows to figuring out the evolution of the French railway landscape, and how it has been impacted by major events (eg.: WWII), or longer time span processes : industry outsourcing, metropolization, public transport policies or absence of them. This work is resulting from the fusion of several public geographical data (SNCF, IGN), enriched with the computer-assisted addition of multiple data gathered on the Internet (Wikipedia, volunteer geographic information). The dataset compounds almost every rail stations (even simple stops) and railway branch nodes, whose link to their respective rail lines allows to build the underlying consistent graph of the network. Every rail line has a "valid to" date (or approx) so that time evolution can be displayed. The present progress of that reconstruction sums up to roughly 90% of what is expected (exact total unknown). This allows to consider temporal demographic analysis (how many cities and towns served by the railway since 1925 up on today), and environmental simulations as well (CO2 cost by given destination ).

The objective is to present one important aspect of the European IST-FET project "REV!GIS"1: the methodology which has been developed for the translation (interpretation) of the quality of the data into a "fitness for use" information, that we can confront to the user needs in its application. This methodology is based upon the notion of "ontologies" as a conceptual framework able to capture the explicit and implicit knowledge involved in the application. We do not address the general problem of formalizing such ontologies, instead, we rather try to illustrate this with three applications which are particular cases of the more general "data fusion" problem. In each application, we show how to deploy our methodology, by comparing several possible solutions, and we try to enlighten where are the quality issues, and what kind of solution to privilege, even at the expense of a highly complex computational approach. The expectation of the REV!GIS project is that computationally tractable solutions will be available among the next generation AI tools.