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GeoBench: a geospatial integration tool for building a spatial entity matching benchmark

Published: 04 November 2014 Publication History

Abstract

In the last decade, a large market for location-based services and geospatial applications has emerged. Multiple cartographic providers propose their visualization tool for displaying points of interests. However, the data describing spatial entities is often incomplete and contradictory from one provider to another, thus limiting further applications such as geospatial data mining. Recent works in entity matching tackle this issue by discovering correspondences between spatial entities that refer to the same real world location. To evaluate and compare these works, we propose GeoBench, a tool which facilitates the building of a benchmark for spatial entity matching.

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{KSG07} Hyunmo Kang, Vivek Sehgal, and Lise Getoor. Geoddupe: A novel interface for interactive entity resolution in geospatial data. In IV, pages 489--496, 2007.
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{Olt07} AM Olteanu. A multi-criteria fusion approach for geographical data matching. International Symposion in Spatial Data Quality, 2007.
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{Res99} Philip Resnik. Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research, 11: 95--130, 1999.
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{SGV06} Vivek Sehgal, Lise Getoor, and Peter Viechnicki. Entity resolution in geospatial data integration. In Rolf A. de By and Silvia Nittel, editors, GIS, pages 83--90. ACM, 2006.
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{SKS+10} Eliyahu Safra, Yaron Kanza, Yehoshua Sagiv, Catriel Beeri, and Yerach Doytsher. Location-based algorithms for finding sets of corresponding objects over several geo-spatial data sets. International Journal of Geographical Information Science, 24(1):69--106, 2010.

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Information & Contributors

Information

Published In

cover image ACM Conferences
SIGSPATIAL '14: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
November 2014
651 pages
ISBN:9781450331319
DOI:10.1145/2666310
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2014

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Author Tags

  1. entity matching benchmark
  2. spatial data quality
  3. spatial entity matching
  4. spatial integration

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  • Research-article

Conference

SIGSPATIAL '14
Sponsor:
  • University of North Texas
  • Microsoft
  • ORACLE
  • Facebook
  • SIGSPATIAL

Acceptance Rates

SIGSPATIAL '14 Paper Acceptance Rate 39 of 184 submissions, 21%;
Overall Acceptance Rate 219 of 1,026 submissions, 21%

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Cited By

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  • (2022)Geospatial Entity ResolutionProceedings of the ACM Web Conference 202210.1145/3485447.3512026(3061-3070)Online publication date: 25-Apr-2022
  • (2022)Multi-Source Spatial Entity LinkageIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299049134:3(1344-1358)Online publication date: 1-Mar-2022
  • (2021)GEMProceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3483973(346-349)Online publication date: 2-Nov-2021
  • (2020)A Deep Learning Approach to Geographical Candidate Selection through Toponym MatchingProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422236(385-388)Online publication date: 3-Nov-2020
  • (2019)Multi-Source Spatial Entity LinkageProceedings of the 16th International Symposium on Spatial and Temporal Databases10.1145/3340964.3340979(1-10)Online publication date: 19-Aug-2019

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