A formalization for semantic location granules

Location-based services have become an increasingly interesting research area in the last two decades. However, in many scenarios, dealing with the most precise location coordinates is not the best solution since people structure the world in geographic areas instead of coordinates. Since humans work with abstractions, and names are the way we refer to those abstractions, introducing semantics in geographic definitions becomes natural. For example, users can be interested in states with vacation resorts and may want to retrieve the state names, instead of the exact geographic limits of such states. Moreover, semantics introduces new challenges, such as how to exploit the location semantics to infer new information from known definitions. For instance, we may want a system to automatically obtain the value added tax (VAT) that should be applied by a shop in Madrid, inferring the applicable tax by considering the economic area where Madrid is included (in this case, Spain); notice that the VAT should not be inferred from a bigger economic area, like Europe, although it also includes Madrid geographically. Thus, the expression of locations at different granularities extends the traditional location-based query processing to consider the most appropriate semantics for each user. In this article, adopting description logics (DLs) as a base formalism, we provide a formalization of the notion of semantic location granule and semantic granule map. We benefit from the underlying semantics of the different granularities to extend the expressivity of location-based queries and automatically discover and infer new knowledge. The model we propose uses a DL reasoner to infer new granules relationships. In particular, a DL reasoner can infer containment and intersection relationships between location granules (and help to obtain several more relationships), which provides the way to introduce semantics in location-based queries. This is done within the logical frame of DLs, thus ensuring that our approach can be supported by existing regular DL reasoners (such as Pellet, Racer Pro, and HermiT) without the need to extend their reasoning capabilities.