Enhanced Geographically-Typed Semantic Schema Matching

Jeffrey Partyka, Pallabi Parveen, Latifur Khan, Bhavani Thuraisingham, Shashi Shekhar
2011 Social Science Research Network  
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
more » ... pared 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. We show the effectiveness of our approach over the traditional approaches across multi-jurisdictional datasets by generating impressive results.
doi:10.2139/ssrn.3199506 fatcat:62tjzqiqg5abnovgixgl7t5qqa