Constructing Semantically Scalable Cognitive Spaces

  • William Pike
  • Mark Gahegan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2825)


This paper describes a technique for creating generalizable depictions of cognitive spaces from natural language documents, and presents a Web-based system that uses this procedure to visualize structure in geographic discourse. We implement a concept abstraction routine that leverages a lexical ontology to infer the semantics of discussion terms at increasing levels of generalization. A Web discussion medium that uses the Delphi method to guide geographic discourse serves as the framework from which concept structures are elicited. Delphi discussants explore these structures using two Web-enabled visualization schemes: Self-Organizing Maps and concept graphs. These visualization tools rely on a set of concept similarity measures tailored to conceptual information at multiple levels of abstraction. The cognitive spaces produced using this system can reveal key themes in a domain, and can help guide the creation of domain ontologies. We apply these tools to explore concept structures in the field of human-environment interaction.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Guarino, N.: Understanding, building, and using ontologies. International Journal of Human- Computer Studies 46, 293–310 (1997)Google Scholar
  2. 2.
    Chen, H., et al.: Information visualization for collaborative computing. IEEE Computer, 75–82 (1998)Google Scholar
  3. 3.
    Turoff, M., Hiltz, S.: Computer based Delphi processes. In: Adler, M., Ziglio, E. (eds.) Gazing into the Oracle: The Delphi Method and its Application to Social Policy and Public Health, Kingsley, London (1996)Google Scholar
  4. 4.
    Fabrikant, S., Buttenfield, B.: Formalizing semantic spaces for information access. Annals of the Association of American Geographers 91(2), 263–280 (2001)Google Scholar
  5. 5.
    Miller, N., et al.: Topic Islands - A wavelet-based text visualization system. In: Proceedings of IEEE Visualization 1998, pp. 189–196 (1998)Google Scholar
  6. 6.
    Havre, S., Hetzler, B., Nowell, L.: ThemeRiver: Visualizing theme changes over time. In: Proceedings of IEEE Symposium on Information Visualization, InfoVis 2000, pp. 115–123 (2000)Google Scholar
  7. 7.
    Feldman, R., et al.: Trend graphs: Visualizing the evolution of concept relationships in large document collections. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 38–46. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  8. 8.
    Redeker, G.: Coherence and structure in text and discourse. In: Bunt, H.C., Black, W.J. (eds.) Abduction, Belief and Context in Dialogue, pp. 233–264. John Benjamins, Philadelphia (2000)Google Scholar
  9. 9.
    Smith, B., Mark, D.M.: Geographical categories: an ontological investigation. International Journal of Geographical Information Science 15(7), 591–612 (2001)Google Scholar
  10. 10.
    Fonseca, F., et al.: Semantic granularity in ontology-driven geographic information systems. Annals of Mathematics and Artificial Intelligence 36(1-2), 121–151 (2002)Google Scholar
  11. 11.
    Frank, A.U.: Tiers of ontology and consistency constraints in geographical information systems. International Journal of Geographical Information Science, 2001 15(7), 667–678 (2001)CrossRefGoogle Scholar
  12. 12.
    Rodriguez, M.A., Egenhofer, M.J.: Determing semantic similarity among entity classes from different ontologies. IEEE Transactions on Knowledge and Data Engineering 15(2), 442–456 (2003)CrossRefGoogle Scholar
  13. 13.
    Fellbaum, C. (ed.): WordNet: An electronic lexical database, p. 433. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  14. 14.
    Mani, I.: Automatic summarization. Natural Language Processing, p. 285. John Benjamins, Philadelphia (2001)Google Scholar
  15. 15.
    Lin, C.-Y.: Topic identification by concept generalization. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics (ACL 1995), pp. 308–310. Association for Computational Linguistics, New Brunswick (1995)Google Scholar
  16. 16.
    Kohonen, T.: Self-organizing maps, p. 426. Berlin, New York (1997)zbMATHGoogle Scholar
  17. 17.
    Garson, G.: Neural networks: an introductory guide for social scientists, p. 194. Sage, London (1998)Google Scholar
  18. 18.
    Sammon, J.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, C-18(5), 401–08 (1969)Google Scholar
  19. 19.
    Kaski, S., et al.: WEBSOM - Self-organizing maps of document collections. Neurocomputing 21, 101–117 (1998)Google Scholar
  20. 20.
    Sowa, J.: Knowledge Representation: Logical, Philosophical, and Computational Foundations, p. 594. Brooks/Cole, Pacific Grove (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • William Pike
    • 1
  • Mark Gahegan
    • 1
  1. 1.GeoVISTA Center, Department of GeographyPennsylvania State UniversityUniversity ParkUSA

Personalised recommendations