LAIR: location awareness information representation

Current location representations model only the geographical aspects of a place. While this is a necessary feature to capture, it is far from sufficient. As a result, many location-aware applications reason about space at the level of coordinates and containment relationships, but have no means to express the semantics that define how a particular space is used. The latter is particularly important in modeling location in the pervasive computing domain. We present LAIR, an ontology that addresses this problem by modeling both the geographical and topological relationships between spaces, as well as the functional purpose of a given space.