Ontology-supported automatic service chaining for geospatial knowledge discovery

With the advances in sensor and platform technologies, the capability for collecting geospatial data has significantly increased. Large volumes of data have been collected using remote sensing. While those data are potentially valuable for the benefit of society, they must be converted to geospatial knowledge before they are useful. The traditional methods - only geospatial experts analyze data - fall far short of today's increased demands for geospatial knowledge. As a result, significant amounts of data have not even once been analyzed after collection. Recent progress in the geospatial semantic Web has shown promise for developing automatic geospatial knowledge discovery methods for solving application problems, which otherwise require considerable resources. This paper presents an approach for automatically solving geospatial problems in the geospatial semantic Web environment. The approach simulates the process used by geospatial experts who first use backward reasoning from the required knowledge to the available raw data to select a set of available geo-processing functions, and then execute the functions sequentially, starting from raw data, to derive the desired knowledge. This backward reasoning effectively creates a path from raw geospatial data to the desired geospatial knowledge. With rich semantic descriptions of services and the support of ontology, the path can be formed automatically through backward reasoning from the desired result to raw geospatial data using semantic Web services. Such a path can be instantiated to become an executable workflow to generate the result automatically. A prototypical system is implemented to demonstrate the above concept and approach.