Spatial concept learning and inference on geospatial polygon data

Geospatial knowledge has always been an essential driver for many societal aspects. This concerns in particular urban planning and urban growth management. To gain insights from geospatial data and guide decisions usually authoritative and open data sources are used, combined with user or citizen sensing data. However, we see a great potential for improving geospatial analytics by combining geospatial data with the rich terminological knowledge, e.g., provided by the Linked Open Data Cloud. Having semantically explicit, integrated geospatial and terminological knowledge, expressed by means of established vocabularies and ontologies, cross-domain spatial analytics can be performed. One analytics technique working on terminological knowledge is inductive concept learning, an approach that learns classifiers expressed as logical concept descriptions. In this paper, we extend inductive concept learning to infer and make use of the spatial context of entities in spatio-terminological data. We propose a formalism for extracting and making spatial relations explicit such that they can be exploited to learn spatial concept descriptions, enabling ‘spatially aware’ concept learning. We further provide an implementation of this formalism and demonstrate its capabilities in different evaluation scenarios.