A semantic approach to retrieving, linking, and integrating heterogeneous geospatial data

Authors:
Ying Zhang;Yao-Yi Chiang;Pedro Szekely;Craig A. Knoblock
Affiliations:
Power University, Beijing, China;University of Southern California, California;University of Southern California, California;University of Southern California, California
Venue:
Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities
Year:
2013

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Abstract

There is a tremendous amount of geospatial data available, and there are numerous methods for extracting, processing and integrating geospatial sources. However, end-users' ability to retrieve, combine, and integrate heterogeneous geospatial data is limited. This paper presents a new semantic approach that allows users to easily extract, link, and integrate geospatial data from various sources by demonstration in an interactive interface, which is implemented in a tool called Karma. First, we encapsulate the retrieval algorithms as web services and invoke the services to extract geospatial data from various sources. Then we model and publish the extracted geospatial data to RDF for eliminating the data heterogeneity. Finally, we link the geospatial data (in RDF) from different sources using a semantic matching algorithm and integrate them using SPARQL queries. This approach empowers end users to rapidly extract geospatial data from diverse sources, to easily eliminate heterogeneity and to semantically link and integrate sources.