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Geographically-typed semantic schema matching

Published: 04 November 2009 Publication History

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 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.

References

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Luiz André P. Paes Leme, Marco A. Casanova, Karin Koogan Breitman, Antonio L. Furtado: Instance-Based OWL Schema Matching. ICEIS 2009: 14--26.
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Daniela F. Brauner, Chantal Intrator, João Carlos Freitas, Marco A. Casanova: An Instance-based Approach for Matching Export Schemas of Geographical Database Web Services. GeoInfo 2007: 109--120.
[3]
E. Rahm and P. A. Bernstein, "A survey of approaches to automatic schema matching", VLDB Journal, vol. V10, pp. 334--350, 2001.
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Bing Tian Dai, Nick Koudas, Divesh Srivastava, Anthony K. H. Tung, and Suresh Venkatasubramanian, "Validating Multi-column Schema Matchings by Type," 24th International Conference on Data Engineering (ICDE), 2008.
[5]
Changqing Zhou, Dan Frankowski, Pamela J. Ludford, Shashi Shekhar, Loren G. Terveen: Discovering personal gazetteers: an interactive clustering approach. GIS 2004: 266--273.
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www.geonames.org
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Jeffrey Partyka, Neda Alipanah Latifur Khan, Bhavani Thuraisingham and Shashi Shekhar, "Content-based Ontology Matching for GIS Datasets", In Proc 16th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems (ACM GIS 2008) November, 2008, Irvine, CA, USA.
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http://www.ertico.com/en/about_ertico/links/gdf_-_geographic_data_files.htm
[9]
Rudi Cilibrasi, Paul M. B. Vitányi: The Google Similarity Distance CoRR abs/cs/0412098:(2004)

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cover image ACM Conferences
GIS '09: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
November 2009
575 pages
ISBN:9781605586496
DOI:10.1145/1653771
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2009

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Author Tags

  1. gazetteer
  2. geographic information systems
  3. schema matching
  4. semantic similarity
  5. type extraction

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GIS '09
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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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