Enhanced geographically typed semantic schema matching

  • Authors:

  • Jeffrey Partyka;Pallabi Parveen;Latifur Khan;B. Thuraisingham;Shashi Shekhar

  • Affiliations:

  • Department of Computer Science, University of Texas at Dallas, 800 West Campbell Rd., Richardson, TX 75080-3021, USA;Department of Computer Science, University of Texas at Dallas, 800 West Campbell Rd., Richardson, TX 75080-3021, USA;Department of Computer Science, University of Texas at Dallas, 800 West Campbell Rd., Richardson, TX 75080-3021, USA;Department of Computer Science, University of Texas at Dallas, 800 West Campbell Rd., Richardson, TX 75080-3021, USA;Department of Computer Science, University of Minnesota, 4-192 EE/CS Bldg, 200 Union St. SE, Minneapolis, MN, USA

  • Venue:
  • Web Semantics: Science, Services and Agents on the World Wide Web
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 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.