Advertisement

SPARQS: Automatic Reasoning in Qualitative Space

  • Baher A. EI-Geresy
  • Alia I. Abdelmoty

Abstract

In this paper the design and implementation of a general qualitative spatial reasoning engine (SPARQS) is presented. Qualitative treatment of information in large spatial databases is used to complement the quantitative approaches to managing those systems, in particular, it is used for the automatic derivation of implicit spatial relat ionships and in maintaining the integrity of the database. To be of practical use, composition tables of spatial relationships between different types of objects need to be developed and integrated in those systems . The automatic derivation of such tables is considered to be a major challenge to current reasoning approaches. In this paper, this issue is addressed and a new approach to the automatic derivation of composition tables is presented. The method is founded on a sound set-theoretical approach for the representation and reasoning over randomly shaped objects in space. A reasoning engine tool, SPARQS, has been implemented to demonstrate the validity of the approach . The engine is composed of a basic graphical interface where composition tables between the most common types of spatial objects is built . An advanced interface is also provided, where users are able to describe shapes of arbitrary complexity and to derive the composition of chosen spatial relationships. Examples of the application of the method using different objects and different types of spatial relationships is presented and new composition tables are built using the reasoning engine.

Keywords

Geographic Information System Spatial Relation Spatial Object Topological Relation Spatial Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    El-Geresy B.A. and Abdelmoty A.I. Order in Space: A General Formalism for Spatial Reasoning. Int. J. on Artificial Intelligence Tools, 6(4):423–450, 1997.CrossRefGoogle Scholar
  2. [2]
    B. Bennett. Spatial Reasoning with Propositional Logics. In Principles of Knowledge Representation and Reasoning (KR94), pages 51–62. Morgan Kaufmann, 1994.Google Scholar
  3. [3]
    B. Bennett, A. Isli, and A. Cohn. When does a composition table provide a complete and tractable proof procedure for a relational constraint language, 1997.Google Scholar
  4. [4]
    I. Bloch. Fuzzy Relative Position between Objects in Image Processing: a Morphological Approach. IEEE Translations on Pattern Analysis and Machine Intelligence, 21(7):657–664, 1999.CrossRefGoogle Scholar
  5. [5]
    A.G.P. Brown and F.P. Coenen. Spatial reasoning: improving computational efficiency. Automation in Construction, 9(4):361–36, 2000.CrossRefGoogle Scholar
  6. [6]
    A. Clementini and P. Di Felice. A Model for Representing Topological Relationships Between Complex Gemetric Features in Spatial Databases. Information Sciences, 3:149–178, 1995.CrossRefGoogle Scholar
  7. [7]
    E. Clementini and P. Di Felice. An Algebraic Model for Spatial Objects with Indeterminate Boundaries. In P.A. Burrough and A.U. Frank, editors, Geographic Objects with Indeterminate Boundaries, GISDATA, pages 155–169. Taylor & Francis, 1996.Google Scholar
  8. [8]
    E. Clementini, P. Di Felice, and G. Califano. Composite Regions in Topological Queries. Information Systems, 20(7):579–594, 1995.CrossRefGoogle Scholar
  9. [9]
    F.P. Coenen and V. Pepijn. A generic ontology for spatial reasoning. In Research and Development in Expert Systems XV, proc. of ES’98, pages 44–57. Springer Verlag, 1998.Google Scholar
  10. [10]
    A.G. Cohn and SM Hazarika. Qualitative spatial representation and reasoning: An overview. Fumdamenta Informaticae, 46(1-2):1–29, 2001.MathSciNetzbMATHGoogle Scholar
  11. [11]
    A.G. Cohn, B. Bennett, J. Gooday, and N.M. Gotts. Qualitative Spatial Representation and Reasoning with the Region Connection Calculus. Geoinformatica, 1(3):1–42, 1997.CrossRefGoogle Scholar
  12. [12]
    A.G. Cohn and N.M. Gotts. The “Egg-Yolk” Representation of Regions with Indeterminate Boundaries. In P.A. Burrough and A.U. Frank, editors, Geographic Objects with Indeterminate Boundaries, GISDATA, pages 171–187. Taylor & Francis, 1996.Google Scholar
  13. [13]
    A.G. Cohn, D.A. Randell, Z. Cui, and B. Bennet. Qualitative Spatial Reasoning and Representation. In P. Carrete and M.G. Singh, editors, Qualitative Reasoning and Decision Technologies, pages 513–522, 1993.Google Scholar
  14. [14]
    M.J. Egenhofer. Deriving the composition of Binary Topological Relations. Journal of Visual Languages and Computing, 5:133–149, 1994.CrossRefGoogle Scholar
  15. [15]
    M.J. Egenhofer, E. Clementini, and Di Felicem P. Topological Relations Between Regions With Holes. Int. J. Geographic Information Systems, 8(2):129–142, 1994.CrossRefGoogle Scholar
  16. [16]
    M.J. Egenhofer and R.D. Franzosa. PointSet Topological Spatial Relations. Int. J. Geographic Information Systems, 5(2): 161–174, 1991.CrossRefGoogle Scholar
  17. [17]
    M.J. Egenhofer and J.R. Herring. A Mathematical Framework for the Definition of Topological Relationships. In Proceedings of the 4th international Symposium on Spotial Data Handling, volume 2, pages 803–13, 1990.Google Scholar
  18. [18]
    H.W. Guesgen and J. Albrech. Imprecise reasoning in geographic information systems. Fuzzy Sets and Systems, 113(1):121–131, 2000.zbMATHCrossRefGoogle Scholar
  19. [19]
    Guesgen, H.W. Spatial reasoning based on allen’ s temporal logic. Technical Report TR-89-049, International Computer Science Institute, Berkeley, California, 1989.Google Scholar
  20. [20]
    F. Karbou. An interval approach to fuzzy surroundedness and fuzzy spatial relations. In 19th International Conference of the North American Fuzzy Information Proauing Society, 2000.Google Scholar
  21. [21]
    Joy Lee and F-J Hsu. Picture Algebra for Spatial Reasoning oflconic Images Represented in 2D G-string. Pattern Recognition, 12:425–435, 1991.CrossRefGoogle Scholar
  22. [22]
    A. Mukerjee and G. Joe. A Qualitative Model for Space. In Proceeding of the 5th National Conference on Artificial Intelligence, AAAI, 1990, pages 721–727, 1990.Google Scholar
  23. [23]
    V.H. Nguyen, C. Parent, and S. Spaceapietra. Complex Regions in Topological Queries. In Proceeding of the Internation Conference on Spatial Information Theory COSIT’97, volume LNCS 1329, pages 175–192. Springer Verlag, 1997.Google Scholar
  24. [24]
    D. Papadias and T. Sellis, Spatial reasoning using symbolic arrays. In Theories and Methods of SpatioTemporal Reasoning in Geographic Space, LNCS 716, pages 153–161. Springer Verlag, 1992.CrossRefGoogle Scholar
  25. [25]
    D. Randell and M. Wikowski. Building Large Composition Tables via Axiomatic Theories. In Principles of Knowledge Representation and Reasoning: Proceedings of the Eighth International Conference (KR-SOOS), pages 26–35. AAAI Press, 2002.Google Scholar
  26. [26]
    D.A. Randell, A.G. Cohn, and Z. Cni. Computing Transitivity Tables: A Challenge for Automated Theorem Provers. In Proceedings of CADE 11, Lecture Notes In Computer Science, 1992.Google Scholar
  27. [27]
    K. Sridharan and H.E. Stephanou. Fuzzy distances for proximity characterization under uncertainty. Fuzzy Sets and Systems, 103(3):427–434, 1999.MathSciNetzbMATHCrossRefGoogle Scholar
  28. [28]
    M.F. Worboys and E. Clementini. Integration of imperfect spatial information. Journal of Visual Languages and Computing, 12:61–80, 2001.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Baher A. EI-Geresy
    • 1
  • Alia I. Abdelmoty
    • 2
  1. 1.School of ComputingUniversity of GlamorganTreforestWales, UK
  2. 2.Department of Computer ScienceCardiff UniversityCardiffWales, UK

Personalised recommendations