According to the authors of a study from Richardson, United States, "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)(1) by combining two separate methods. Our primary method discerns the geographic types from instances of compared attributes. If successful, EBD is calculated using only this method. GSim further facilitates geographic type matching by using latlong values to further disambiguate between multiple types of a given instance and applying attribute weighting to quantify the uniqueness of mapped attributes. If geographic type matching is not possible, we then apply a generic schema matching method, independent of the knowledge domain, which employs normalized Google distance," wrote J. Partyka and colleagues.
The researchers concluded: "We show the effectiveness of our approach over the traditional approaches across multi-jurisdictional datasets by generating impressive results."
Partyka and colleagues published the results of their research in the Journal of Web Semantics (Enhanced geographically typed semantic schema matching. Journal of Web Semantics, 2011;9(1):52-70).
For additional information, contact L. Khan, University of Texas Dallas, Dept. of Computational Science, 800 W Campbell Rd., Richardson, TX 75080, United States.
The publisher of the Journal of Web Semantics can be contacted at: Elsevier Science BV, PO Box 211, 1000 AE Amsterdam, Netherlands.
Keywords: City:Richardson, State:Texas, Country:United States, Region:North and Central America, World Wide Web
This article was prepared by Internet Weekly News editors from staff and other reports. Copyright 2011, Internet Weekly News via VerticalNews.com.
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