research-article

Data mining of maps and their automatic region-time-theme classification

Author:

Judith GelernterAuthors Info & Claims

SIGSPATIAL Special, Volume 1, Issue 1

Pages 39 - 44

https://doi.org/10.1145/1517463.1517468

Published: 01 March 2009 Publication History

Abstract

The goal of this research is to organize maps mined from journal articles into categories for hierarchical browsing within region, time and theme facets. A 150-map training set collected manually was used to develop classifiers. Metadata pertinent to the maps were harvested and then run separately though knowledge sources and our classifiers for region, time and theme. Evaluation of the system based on a 54-map test set of unseen maps showed 69%--93% classification accuracy when compared with two human classifications for the same maps. Data mining and semantic analysis methods used here could support systems that index other types of article components such as diagrams or charts by region, time and theme.

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Cited By

Lee CYang HWang S(2010)A location based text mining method using ANN for geospatial KDD processProceedings of the 7th international conference on Advances in Neural Networks - Volume Part II10.1007/978-3-642-13318-3_37(292-301)Online publication date: 6-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13318-3_37
Chiang YKnoblock CWolfson OAgrawal DLu C(2009)Classification of raster maps for automatic feature extractionProceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/1653771.1653793(138-147)Online publication date: 4-Nov-2009
https://dl.acm.org/doi/10.1145/1653771.1653793
Lee CYang HWang S(2009)A Location Based Text Mining Approach for Geospatial Data MiningProceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control10.1109/ICICIC.2009.23(1172-1175)Online publication date: 7-Dec-2009
https://dl.acm.org/doi/10.1109/ICICIC.2009.23

Index Terms

Data mining of maps and their automatic region-time-theme classification
1. Information systems
  1. Information retrieval
    1. Document representation
      1. Thesauri
    2. Search engine architectures and scalability
      1. Search engine indexing

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cover image SIGSPATIAL Special

SIGSPATIAL Special Volume 1, Issue 1

March 2009

49 pages

EISSN:1946-7729

DOI:10.1145/1517463

Issue’s Table of Contents

Copyright © 2009 Copyright is held by the owner/author(s).

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

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Published: 01 March 2009

Published in SIGSPATIAL Volume 1, Issue 1

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Cited By

Lee CYang HWang S(2010)A location based text mining method using ANN for geospatial KDD processProceedings of the 7th international conference on Advances in Neural Networks - Volume Part II10.1007/978-3-642-13318-3_37(292-301)Online publication date: 6-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13318-3_37
Chiang YKnoblock CWolfson OAgrawal DLu C(2009)Classification of raster maps for automatic feature extractionProceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/1653771.1653793(138-147)Online publication date: 4-Nov-2009
https://dl.acm.org/doi/10.1145/1653771.1653793
Lee CYang HWang S(2009)A Location Based Text Mining Approach for Geospatial Data MiningProceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control10.1109/ICICIC.2009.23(1172-1175)Online publication date: 7-Dec-2009
https://dl.acm.org/doi/10.1109/ICICIC.2009.23

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