A Generic Ontology for Spatial Reasoning

  • Frans Coenen
  • Pepijn Visser
  • CORAL Research Group


In this paper we describe a generic ontology to support N-dimensional spatial reasoning applications. The ontology is intended to support both quantitative and qualitative approaches and is expressed using set notation. Using the ontology; spatial domains of discourse, spatial objects and their attributes, and the relationships that can link spatial objects can be expressed in terms of sets, and sets of sets. The ontology has been developed through a series of application studies. For each study a directed application ontology was first developed which was then merged into the generic ontology. Application areas that have been investigated include: Geographic Information Systems (GIS), noise pollution monitoring, environmental impact assessment, shape fitting, timetabling and scheduling, and AI problems such as the N-queens problem.


Generic Ontology Geographic Information System Geographic Information System Spatial Object Location Space 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Beattie, B., Coenen, F.P., Hough, A., Bench-Capon, T.J.M., Diaz, B.M. and Shave, M.J.R. (1996). Spatial Reasoning for Environmental Impact Assessment. Third International Conference on GIS and Environmental Modeling, Santa Barbara: National Center for Geographic Information and Analysis, WWW and CD.Google Scholar
  2. [2]
    Brown, A.G.P., Coenen, F.P., Shave, M.J. and Knight, M.W. (1998). An AI Approach to Noise Prediction. To appear in Building Acoustics.Google Scholar
  3. [3]
    Coenen, F.P., Beattie, B., Bench-Capon, T.J.M., Shave, M.J.R. and Diaz, B.M. (1995). Spatial Reasoning for Timetabling: The TIMETABLER system. Proceedings of the 1st International Conference on the Practice and Theory of Automated Timetabling (ICPTAT’95), Napier University, Edinburgh, pp57–68.Google Scholar
  4. [4]
    Coenen, F.P., Beattie, B., Bench-Capon, T.J.M., Diaz, B.M. and Shave, M.J.R. (1996). Spatial Reasoning for Geographic Information Systems. Proceedings 1st International Conference on GeoComputation, School of Geography, University of Leeds, Vol 1, pp121–131.Google Scholar
  5. [5]
    Coenen, F.P., Beattie, B., Bench-Capon, T.J.M., Diaz, B.M. and Shave. M.J.R. (1997). A tesseral Approach to N-Dimensional Spatial Reasoning. In Hameurlain, A. and Tjoa, A.M. (Eds), Database and Expert Systems Applications, (Proceedings DEXA’97), Lecture Notes in Computer Science 1308, Springer Verlag, pp633–642.Google Scholar
  6. [6]
    Coenen, F.P., Beattie, B., Bench-Capon, T.J.M., Diaz, B.M. and Shave. M.J.R. (1998). Spatio-Temporal Reasoning Using A Multi-Dimensional Tesserai Representation. Proceedings ECAI’98, pp140–144.Google Scholar
  7. [7]
    Diaz, B.M. and Bell, S.B.M. (1987). Spatial Data Processing using Tesseral Methods. Natural Environment Research Council publication, Swindon, England.Google Scholar
  8. [8]
    Gruber, T. (1992). Ontolingua: A Mechanism to Support Portable Ontologies. Knowledge Systems Laboratory, Standford University, Stanford, USA.Google Scholar
  9. [9]
    Heijst, G. van (1995). The Role of Ontologies in Knowledge Engineering, Doctoral Thesis, University of Amsterdam, Amsterdam, The Netherlands.Google Scholar
  10. [10]
    Heijst, G. Van, and G. Schreiber (1994). CUE: Ontology-based knowledge acquisition, L. Steels, A. Th. Schreiber, and W. Van de Velde (eds.), A Future for Knowledge Acquisition, Proceedings of the 8th European Knowledge Acquisition Workshop EKAW’94, Vol. 867 of Lecture Notes in Artificial Intelligence, pp.178–199, Springer-Verlag, Berlin/Heidelberg, Germany.Google Scholar
  11. [11]
    Kuokka, D.R., J. McGuire, J.C. Weber, J.M. Tenenbaum, T.R. Gruber, and G.R. Olsen (1993). SHADE: Knowledge-Based Technology for the Re-Engineering Problem, Annual Report 1993.Google Scholar
  12. [12]
    Leher, N. (1990). Knowledge Representations Specification Language. Technical Report, DARPA/Rome Laboratory Planning and Scheduling Initiative, Reference manual.Google Scholar
  13. [13]
    Lenat, D.B. and R.V. Guha (1990). Building Large Knowledge-Based Systems; Representation and Reasoning in the Cyc Project, Addison-Wesley, Reading, Massachusetts, United States.Google Scholar
  14. [14]
    Morik, K., S. Wrobel, J-U. Kietz, and W. Emde (1993). Knowledge Acquisition and Machine Learning; Theory, Methods and Applications, Knowledge-Based Systems, Academic Press Limited, London, United Kingdom.Google Scholar
  15. [15]
    Sim, I., and G. Rennels (1995). Developing A Clinical Trial Ontology: Comments on Domain Modeling and Ontological Reuse, Knowledge Systems Laboratory Medical Computer Science, KSL-95-60, June 1995.Google Scholar
  16. [16]
    Simons, P. (1987). Parts — A study in Ontology. Clarendon Press, Oxford.Google Scholar
  17. [17]
    Visser, P.R.S. and T.J.M. Bench-Capon (1998). A Comparison of Four Ontologies for the Design of Legal Knowledge Systems, Artificial Intelligence and Law, Vol 6, No 1, pp3–26.CrossRefGoogle Scholar
  18. [18]
    Wiederhold, G. (1994). Interoperation, Mediation, and Ontologies, Proceedings International Symposium on Fifth Generation Computer Systems (FGCS94), Workshop on Heterogeneous Cooperative Knowledge-Bases, Vol. W3, pp.33–48, ICOT, Tokyo, Japan.Google Scholar

Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • Frans Coenen
    • 1
  • Pepijn Visser
    • 1
  • CORAL Research Group
    • 1
  1. 1.Department of Computer ScienceThe University of LiverpoolLiverpoolUK

Personalised recommendations