Geographically-typed semantic schema matching
Jeffrey Partyka, Latifur R. Khan, Bhavani M. Thuraisingham · 2009
Resolving semantic heterogeneity across distinct data sources remains a highly relevant problem in the GIS domain requiring innovative solutions. Our approach, called GSim, semantically aligns tables from respective GIS databases by first choosing attributes for comparison. We then examine their instances and calculate a similarity value between them called entropy-based distribution (EBD) by combining two separate methods. Our primary method discerns the geographic types from instances of compared attributes. If geographic type matching is not possible, we then apply a generic schema matching method which employs normalized Google distance. We show the effectiveness of our approach over the traditional N-gram approach across multi-jurisdictional datasets by generating impressive results.