GeoSemantic Web

The vision for the Semantic Web is to enhance information retrieval by incorporating semantics and exploiting the semantics during the search process. Such developments need special attention from the geospatial perspective so that geospatial meanings are captured appropriately. The Geospatial Semantic Web research involves developing multiple spatial and terminological ontologies, each with a formal semantics; representing those semantics for both machine processing and human understanding; and enhancing geospatial queries based on ontologies.

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Example of individual exposure risk instances represented as an rdf graph. Graph displays linked information about the potential risk for a single individual (Person 8) based on location (geographic coordinates), function of location (school, home, work), duration of exposure event (start, end times), the type of event (Noteworthy/Over Threshold Level), and type of toxic agent (Agent 1, 2,6, 10). (Doore, S. A., Beard, K., & Bult, C. (2010, November). An ontology-based personal exposure history. In Proceedings of the 1st ACM International Health Informatics Symposium (pp. 674-683). ACM. doi>10.1145/1882992.1883097)Example of individual exposure risk instances represented as an rdf graph. Graph displays linked information about the potential risk for a single individual (Person 8) based on location (geographic coordinates), function of location (school, home, work), duration of exposure event (start, end times), the type of event (Noteworthy/Over Threshold Level), and type of toxic agent (Agent 1, 2,6, 10). (Doore, S. A., Beard, K., & Bult, C. (2010, November). An ontology-based personal exposure history. In Proceedings of the 1st ACM International Health Informatics Symposium (pp. 674-683). ACM. doi>10.1145/1882992.1883097)

Figure 1 (left): Example of HydroOntology represented as rdf model (Resource Description Framework) that links a water body type (Stream) with its HydroGazetteer schema which includes: attribute properties (Name, AltName), its connectivity properties (Mouth, Source, Tributary), topological properties (FlowsInto) and location properties (Location).
Figure 2 (right): Example of individual exposure risk instances represented as an rdf graph. Graph displays linked information about the potential risk for a single individual (Person 8) based on location (geographic coordinates), function of location (school, home, work), duration of exposure event (start, end times), the type of event (Noteworthy/Over Threshold Level), and type of toxic agent (Agent 1, 2,6, 10).
(Doore, S. A., Beard, K., & Bult, C. (2010, November). An ontology-based personal exposure history. In Proceedings of the 1st ACM International Health Informatics Symposium (pp. 674-683). ACM. doi>10.1145/1882992.1883097)