CFP last date
20 August 2024
Call for Paper
September Edition
IJCA solicits high quality original research papers for the upcoming September edition of the journal. The last date of research paper submission is 20 August 2024

Submit your paper
Know more
Reseach Article

A Fuzzy Logic based Method for Efficient Retrieval of Vague and Uncertain Spatial Expressions in Text Exploiting the Granulation of the Spatial Event Queries

Published on February 2013 by V. R. Kanagavalli, K. Raja
National Conference on Future Computing 2013
Foundation of Computer Science USA
NCFC - Number 1
February 2013
Authors: V. R. Kanagavalli, K. Raja

V. R. Kanagavalli, K. Raja . A Fuzzy Logic based Method for Efficient Retrieval of Vague and Uncertain Spatial Expressions in Text Exploiting the Granulation of the Spatial Event Queries. National Conference on Future Computing 2013. NCFC, 1 (February 2013), 19-25.

author = { V. R. Kanagavalli, K. Raja },
title = { A Fuzzy Logic based Method for Efficient Retrieval of Vague and Uncertain Spatial Expressions in Text Exploiting the Granulation of the Spatial Event Queries },
journal = { National Conference on Future Computing 2013 },
issue_date = { February 2013 },
volume = { NCFC },
number = { 1 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 19-25 },
numpages = 7,
url = { /proceedings/ncfc/number1/10404-1005/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Proceeding Article
%1 National Conference on Future Computing 2013
%A V. R. Kanagavalli
%A K. Raja
%T A Fuzzy Logic based Method for Efficient Retrieval of Vague and Uncertain Spatial Expressions in Text Exploiting the Granulation of the Spatial Event Queries
%J National Conference on Future Computing 2013
%@ 0975-8887
%N 1
%P 19-25
%D 2013
%I International Journal of Computer Applications

The arrangement of things in n-dimensional space is specified as Spatial. Spatial data consists of values that denote the location and shape of objects and areas on the earth's surface. Spatial information includes facts such as location of features, the relationship of geographic features and measurements of geographic features. The spatial cognition is a primal area of study in various other fields such as Robotics, Psychology, Geosciences, Geography, Political Sciences, Geographic Economy, Environmental, Mining and Petroleum Engineering, Natural Resources, Epidemiology, Demography etc. , Any text document which contains physical location specifications such as place names, geographic coordinates, landmarks, country names etc. , are supposed to contain the spatial information. The spatial information may also be represented using vague or fuzzy descriptions involving linguistic terms such as near to, far from, to the east of, very close. Given a query involving events, the aim of this ongoing research work is to extract the relevant information from multiple text documents, resolve the uncertainty and vagueness and translate them in to locations in a map. The input to the system would be a text Corpus and a Spatial Query event. The output of the system is a map showing the most possible, disambiguated location of the event queried. The author proposes Fuzzy Logic Techniques for resolving the uncertainty in the spatial expressions.

  1. C. B. Jones, Ross. S. Purves, 2008. Geographical Information Retrieval, International Journal of Geographical Information Science, 22(3)
  2. S. Kikuchi et al. , Place of possibility theory in transportation analysis. Transportation Research Part B 2006. Elsevier
  3. Rock, Nathaniel Robert. "Mapping geospatial events based on extracted spatial information from web documents. " master's thesis, University of Iowa, 2011. http://ir. uiowa. edu/etd/1068
  4. George J. Klir and Bo Yuan. Fuzzy sets and Fuzzy logic, Theory and applications,
  5. Debra, Rajiv Chopra, Rohini Srihari. Domain Specific Understanding of Spatial Expressions. citeseerx. ist. psu. edu / viewdoc / download ?doi=10
  6. Kate Byrne and Ewan Klein. Automatic Extraction of Archaeological Events from Text. May 2009.
  7. Hans w. guesgen. Reasoning About Distance Based on Fuzzy Sets. Applied Intelligence 17, 265–270, 2002
  8. Thomas Kollar et al. , Toward Understanding Natural Language Directions. Naval Research
  9. Geospatial reasoning in a Natural Language Processing (NLP) Environment. Bitters B.
  10. Fei Song. Bruce Croft. A General Language Model for Information Retrieval
  11. Damien Palacio and Christian Sallaberry and Mauro Gaio. Normalizing spatial information to improve geographical Information indexing and retrieval in digital librarieS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, Part II
  12. Zhang, Q. , 2005. Road Network Generalization Based on Connection Analysis. In: Developments in Spatial Data Handling, Springer Berlin Heidelberg, pp. 343–353.
  13. Zhou, S. and Jones, C. B. , 2004. Shape-Aware Line Generalisation With Weighted Effective Area. In: Developments in Spatial Data Handling 11th International Symposium on Spatial Data Handling, Springer, Springer, pp. 369–380.
  14. Zhou, X. , Zhang, Y. , Lu, S. and Chen, G. , 2000. On Spatial Information Retrieval and Database Generalization. In: Kyoto International Conference on Digital Libraries, pp. 380–386.
  15. Glander, T. and D¨ollner, J. , 2007. Cell-based generalization of 3D building groups with outlier management. In: Hanan Samet and Cyrus Shahabi and Markus Schneider (ed. ), GIS, ACM, p. 54.
  16. Robbins, S. , Evans, A. C. , Collins, D. L. and Whitesides, S. , 2003. Tuning and Comparing Spatial Normalization Methods. In: Randy E. Ellis and Terry M. Peters (ed. ), MICCAI (2), Lecture Notes in Computer Science, Vol. 2879, Springer, pp. 910–917.
  17. Rees, T. , 2003. "C-Squares", a New Spatial Indexing System and its Applicability to the Description of Oceanographic Datasets. In: Oceanography, Vol. 16number 1, pp. 11–19.
  18. Salton, G. and McGill, M. J. , 1983. Introduction to Modern Information Retrieval. McGraw-Hill
  19. Cai, G. , 2002. GeoVSM: An Integrated Retrieval Model for Geographic Information. In: Max J. Egenhofer and David M. Mark (ed. ), GIScience, Lecture Notes in Computer Science, Vol. 2478, Springer, pp. 65–79
  20. Jones, C. B. , Alani, H. and Tudhope, D. , 2001. Geographical Information Retrieval with Ontologies of Place. In: D. R. Montello (ed. ), Conference on Spatial Information Theory - (COSIT 2001), Vol. 2205 / 2001, Springer-Verlag Heidelberg, Morro Bayand California USA, pp. 322–335.
  21. Kalev H. Leetaru Fulltext Geocoding Versus Spatial Metadata for Large Text Archives: Towards a Geographically Enriched Wikipedia . D-Lib Magazine September/October 2012 Volume 18, Number 9/10 doi:10. 1045/september2012-leetaru.
  22. Li,, et al. Research on problem-based spatial and non-spatial information search methods. Wuhan University.
  23. B. Coyne, D. Bauer, and O. Rambow, "VigNet: Grounding language in graphics using frame semantics," in ACL Workshop on Relational Models of Semantics (RELMS), 2011.
  24. Frederico Fonseca et al. , Semantic Granularity in ontology-driven geographic information systems. Annals of Mathematics and artificial intelligence. 2011
  25. Y. Y. Chen, T. Suel, and A. Markowetz. Efficient query processing in geographic web search engines. In SIGMOD Conference, pages 277{288, 2006}.
  26. H. W. Guesgen, J. Albrecht . Imprecise reasoning in geographic information systems Fuzzy Sets and Systems 113 (2000)
  27. V. R. Kanagavalli, K. Raja, Graduated granulation of spatial information for efficient, effective business activity monitoring, Fuzzy Sets and Systems, pp 99-101. 2010
  28. Mulkar-Mehta, R. ; Hobbs, J. R. ; and Hovy, E. 2011. Granularity in Natural Language Discourse. International Conference on Computational Semantics, Oxford, UK 360—-364
  29. Mulkar-Mehta, R. ; Hobbs, J. R. ; and Hovy, E. Applications and Discovery of Granularity Structures in Natural Language Discourse. Logical Formalizations of Commonsense Reasoning — Papers from the AAAI 2011 Spring Symposium (SS-11-06)
  30. N. O. Rubens. The application of fuzzy logic to the construction of the ranking function of information retrieval systems. Computer Modeling and New Technologies, 10(1):20–27, 2006.
  31. Zadeh L. A. (2004) Foreword to Fuzzy Logic Toolbox User's Guide. The MathWorks Inc
Index Terms

Computer Science
Information Sciences


Fuzzy Logic Granulation Possibility Distribution Function Spatial Event Queries