Supporting Geo-Ontology Engineering through Spatial Data Analytics

Geo-ontologies are becoming first-class artifacts in spatial data management because of their ability to represent places and points of interest. Several general-purpose geo-ontologies are available and widely employed to describe spatial entities across the world. The cultural, contextual and geographic differences between locations, however, call for more specialized and spatially-customized geo-ontologies. In order to help ontology engineers in (re)engineering geo-ontologies, spatial data analytics can provide interesting insights on territorial characteristics, thus revealing peculiarities and diversities between places. In this paper we propose a set of spatial analytics methods and tools to evaluate existing instances of a general-purpose geo-ontology within two distinct urban environments, in order to support ontology engineers in two tasks: (1) the identification of possible location-specific ontology restructuring activities, like specializations or extensions, and (2) the specification of new potential concepts to formalize neighborhood semantic models. We apply the proposed approach to datasets related to the cities of Milano and London extracted from LinkedGeoData, we present the experimental results and we discuss their value to assist geo-ontology engineering.