Abstract
In this paper we describe an algorithm for merging ontologies from heterogeneous geographic data sources. The algorithm is based on an asymmetric similarity function that considers the spatial distribution of thematic values in the datasets. It has been used in the context of a semantic framework that provides a set of semantic services to enable external clients to find, translate and integrate thematic information from different geographic datasets in a repository. An optimised version of the algorithm is also described enabling its execution in real time, even with large datasets. The algorithm has been tested in the context of merging datasets with more than 108 spatial units.
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Navarrete, T., Blat, J. (2007). An Algorithm for Merging Geographic Datasets Based on the Spatial Distributions of Their Values. In: Fonseca, F., Rodríguez, M.A., Levashkin, S. (eds) GeoSpatial Semantics. GeoS 2007. Lecture Notes in Computer Science, vol 4853. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76876-0_5
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DOI: https://doi.org/10.1007/978-3-540-76876-0_5
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