Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies.It also promotes sharing and reuse of geospatial d...Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies.It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages,such as RDF,to form geospatial knowledge base.For many applications,rapid retrieval of spatial data from the knowledge base is critical.However,spatial data retrieval using the standard Semantic Web query language–Geo-SPARQL–can be very inefficient because the data in the knowledge base are no longer indexed to support efficient spatial queries.While recent research has been devoted to improving query performance on general knowledge base,it is still challenging to support efficient query of the spatial data with complex topological relationships.This research introduces a query strategy to improve the query performance of geospatial knowledge base by creating spatial indexing on-the-fly to prune the search space for spatial queries and by parallelizing the spatial join computations within the queries.We focus on improving the performance of Geo-SPARQL queries on knowledge bases encoded in RDF.Our initial experiments show that the proposed strategy can greatly reduce the runtime costs of Geo-SPARQL query through on-the-fly spatial indexing and parallel execution.展开更多
基金Anselin’s research was supported in part by award OCI-1047916,SI2-SSI from the US National Science Foundation.
文摘Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies.It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages,such as RDF,to form geospatial knowledge base.For many applications,rapid retrieval of spatial data from the knowledge base is critical.However,spatial data retrieval using the standard Semantic Web query language–Geo-SPARQL–can be very inefficient because the data in the knowledge base are no longer indexed to support efficient spatial queries.While recent research has been devoted to improving query performance on general knowledge base,it is still challenging to support efficient query of the spatial data with complex topological relationships.This research introduces a query strategy to improve the query performance of geospatial knowledge base by creating spatial indexing on-the-fly to prune the search space for spatial queries and by parallelizing the spatial join computations within the queries.We focus on improving the performance of Geo-SPARQL queries on knowledge bases encoded in RDF.Our initial experiments show that the proposed strategy can greatly reduce the runtime costs of Geo-SPARQL query through on-the-fly spatial indexing and parallel execution.
