In this paper we present a Semantic Indoor Trajectory Model aimed at supporting the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we are interested in combining aspects of state-of-the art semantic outdoor trajec-tory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC's In-doorGML standard. We drive the discussion on those modeling issues and details that have been overlooked so far or where our approach deviates from typical practices. We illustrate the modeling part with instantiations from the Louvre Museum in an effort to provide a pragmatic view of what a Semantic Indoor Trajectory Model ought to represent and ideally also how.