Abstract : In-silico experiments, also known as simulations, are traditionally automated through scientific workflows. While those systems effectively tackle the complexity of underlying technologies, they themselves do little to ease and organize sharing and reuse. Indeed, scientific workflow models blur the line between user goals and techniques and often mix abstraction levels. We believe that an intentional model, explicitly differentiating goals from methods to achieve them, is a necessary step towards the creation of an effective sharing and reuse platform for in-silico experiments. Our contribution is threefold: (1) we extended the map model with explicit forks and parameterizable maps, (2) we proposed a map ontology in RDFS that allows computer processing and reasoning on maps and (3) we applied the map model to in-silico experiments in order to emphasize user intentions.