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GeoInformatica

, Volume 9, Issue 1, pp 61–84 | Cite as

Multi-Dimensional Scattered Ranking Methods for Geographic Information Retrieval*

  • Marc van KreveldEmail author
  • Iris Reinbacher
  • Avi Arampatzis
  • Roelof van Zwol
Original Article

Abstract

Geographic Information Retrieval is concerned with retrieving documents in response to a spatially related query. This paper addresses the ranking of documents by both textual and spatial relevance. To this end, we introduce multi-dimensional scattered ranking, where textually and spatially similar documents are ranked spread in the list, instead of consecutively. The effect of this is that documents close together in the ranked list have less redundant information. We present various ranking methods of this type, efficient algorithms to implement them, and experiments to show the outcome of the methods.

Keywords

geographic information retrieval relevance ranking algorithms 

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References

  1. 1.
    ANWB. Campinggids 2, 2002.Google Scholar
  2. 2.
    A. Arampatzis and M. van Kreveld. “Implementation of simple geographic similarity measures.” Report 5102 of the SPIRIT project, 2003.Google Scholar
  3. 3.
    G.S. Brodal and R. Jacob. “Dynamic planar convex hull,” in Proc. 43rd IEEE Sympos. Found. Comput. Sci., 617–626, 2002.Google Scholar
  4. 4.
    J.G. Carbonell and J. Goldstein. “The use of MMR, diversity-based reranking for reordering documents and producing summaries,” in Research and Development in Information Retrieval, 335–336, 1998.Google Scholar
  5. 5.
    T.H. Cormen, C.E. Leiserson, and R.L. Rivest. “Introduction to algorithms.” MIT Press, Cambridge, MA, 1990.Google Scholar
  6. 6.
    M. de Berg, M. van Kreveld, M. Overmars, and O. Schwarzkopf. “Computational Geometry: Algorithms and Applications.” Springer-Verlag, Berlin, 1997.Google Scholar
  7. 7.
    S. Fortune. “Voronoi diagrams and delaunay triangulations,” in D.-Z. Du and F. K. Hwang (Eds.), Computing in Euclidean Geometry, volume 4 of Lecture Notes Series on Computing, 225–265, World Scientific, Singapore, 2nd edition, 1995.Google Scholar
  8. 8.
    J. Goldstein, M. Kantrowitz, V.O. Mittal, and J.G. Carbonell. “Summarizing text documents: Sentence selection and evaluation metrics,” in Research and Development in Information Retrieval, 121–128, 1999.Google Scholar
  9. 9.
    J. Goldstein, V.O. Mittal, J.G. Carbonell, and J.P. Callan. “Creating and evaluating multi-document sentence extract summaries,” in CIKM, 165–172, 2000.Google Scholar
  10. 10.
    D. Harman. “Overview of the TREC 2002 novelty track,” in NISI Special Publication 500–251: Proc. 11th Text Retrieval Conference (TREC 2002), 2002.Google Scholar
  11. 11.
    P.S. Heckbert and M. Garland. “Fast polygonal approximation of terrains and height fields.” Report CMU-CS-95-181, Carnegie Mellon University, 1995.Google Scholar
  12. 12.
    J. Hershberger and S. Suri. “Applications of a semi-dynamic convex hull algorithm,” BIT, Vol. 32:249–267, 1992.Google Scholar
  13. 13.
    C.B. Jones, R. Purves, A. Russ, M. Sanderson, M. Sester, M.J. van Kreveld, and R. Weibel. “Spatial information retrieval and geographical ontologies—an overview of the spirit project,” in Proc. 25th Annu. Int. Conf. on Research and Development in Information Retrieval (SIGIR 2002), 387–388, 2002.Google Scholar
  14. 14.
    G.E. Langran and T.K. Poiker. “Integration of name selection and name placement,” in Proc. 2nd Int. Symp. on Spatial Data Handling, 50–64, 1986.Google Scholar
  15. 15.
    R.R. Larson and P. Frontiera. “Spatial ranking methods for geographic information retrieval (GIR) in digital libraries,” Europ. Conf. on Digital Libraries, 2004.Google Scholar
  16. 16.
    M. H. Overmars. “The Design of Dynamic Data Structured,” volume 156 of Leture Notes Comput. Sci. Springer-Verlag, Heidelberg, West Germany, 1983.Google Scholar
  17. 17.
    E. Rauch, M. Bukatin, and K. Baker. “A confidence-based framework for disambiguating geographic terms,” in A. Kornai and B. Sundheim (Eds.), HLT-NAACL 2003 Workshop: Analysis of Geographic References, 50–54, Edmonton, Alberta, Canada, May 31 2003. Association for Computational Linguistics.Google Scholar
  18. 18.
    R. Seidel. “Constructing higher-dimensional convex hulls at logarithmic cost per face,” in Proc. 18th Annu. ACM Sympos. Theory Comput., 404–413, 1986.Google Scholar
  19. 19.
    M. van Kreveld, R. van Oostrum, and J. Snoeyink. “Efficient settlement selection for interactive display,” in Proc. Auto-Carto 13: ACSM/ASPRS Annual Convention Technical Papers, 287–296, 1997.Google Scholar
  20. 20.
    R. van Zwol, C. Jones, and M. van Kreveld. “Aspects of spatial similatity measures.” Report 5101 of the SPIRIT project, 2003.Google Scholar
  21. 21.
    U. Visser, T. Vögele, and C. Schlieder. “Spatio-terminological information retrieval using the BUSTER system,” in Proc. of the EnviroInfo, 93–100, 2002.Google Scholar
  22. 22.
    D.E. Willard and G.S. Lueker. “Adding range restriction capability to dynamic data structures,” J. ACM, Vol. 32:597–617, 1985.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Marc van Kreveld
    • 1
    Email author
  • Iris Reinbacher
    • 1
  • Avi Arampatzis
    • 1
  • Roelof van Zwol
    • 1
  1. 1.Institute of Information and Computing SciencesUtrecht UniversityThe Netherlands

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