Methodology for geospatial data source discovery in ontology-driven geo-information integration architectures

ion, entity, gathering, abstract_entity, attribute,entity, attribute, physical_entity, object, chance, road, street, opportunity, physical_object, possibility, route, way, neighborhood, state, situation, unit, artifact, social_group, neighbourhood, existence, community, being, grouping, group, assemblage, state_of_affairs, possibleness, environment, beingness, two-way_street, Wall_St., Quai_d'Orsay, Bowery, main_street, avenue, Downing_Street, Park_Ave., boulevard, Park_Avenue, back_street, local_street, Broadway, Great_White_Way, alley, alleyway, oneway_street, local_road, side_street, Fleet_Street, high_street, mews, Wall_Street, cross_street, Strand, Champs_Elysees, rue, Whitehall, Pall_Mall, Lombard_Street, Harley_Street}; this term set is shown within a list (frame 2 on Figure 9). Walkthrough step 3 (frame 3 on Figure 9): • Input: GeoNis.owl ontology; user will activate ‘load ontology’ button within prototype application UI and will be provided with a dialog used to select the appropriate ontology from the file system. • Discovery algorithm step: step 4; concept term set is created by traversing GeoNis ontology. • Output: concept term set: {Accommodation, Cable, Road_Feature, Substation, Taxi_Stop, Populated_Place_Feature, Shopping,