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Investigations into the Cognitive Conceptualization and Similarity Assessment of Spatial Scenes

  • Jan Oliver Wallgrün
  • Jinlong Yang
  • Alexander Klippel
  • Frank Dylla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7478)

Abstract

Formally capturing spatial semantics is a challenging and still largely unsolved research endeavor. Qualitative spatial calculi such as RCC-8 and the 9-Intersection model have been employed to capture humans’ commonsense understanding of spatial relations, for instance, in information retrieval approaches. The bridge between commonsense and formal semantics of spatial relations is established using similarities which are, on a qualitative level, typically formalized using the notion of conceptual neighborhoods. While behavioral studies have been carried out on relations between two entities, both static and dynamic, similar experimental work on complex scenes involving three or more entities is still missing. We address this gap by reporting on three experiments on the category construction of spatial scenes involving three entities in three different semantic domains. To reveal the conceptualization of complex spatial scenes, we developed a number of analysis methods. Our results show clearly that (I) categorization of relations in static scenarios is less dependent on domain semantics than in dynamically changing scenarios, that (II) RCC-5 is preferred over RCC-8, and (III) that the complexity of a scene is broken down by selecting a main reference entity.

Keywords

Semantic Domain Similarity Assessment Small Ellipse Linguistic Description Qualitative Relation 
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.

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References

  1. Anthony, L.: AntConc (version 3.2.2). Waseda University, Tokyo (2011), http://www.antlab.sci.waseda.ac.jp/
  2. Bateman, J.A., Hois, J., Ross, R., Tenbrink, T.: A linguistic ontology of space for natural language processing. Artificial Intelligence 174(14), 1027–1071 (2010)CrossRefGoogle Scholar
  3. Bruns, H.T., Egenhofer, M.J.: Similarity of spatial scenes. In: Kraak, M.J., Molenaar, M. (eds.) Seventh International Symposium on Spatial Data Handling (SDH 1996), Delft, The Netherlands, pp. 173–184 (1996)Google Scholar
  4. Cohn, A.G.: Conceptual neighborhood. In: Shekhar, S., Xiong, H. (eds.) Encyclopedia of GIS, p. 123. Springer, Boston (2008)CrossRefGoogle Scholar
  5. Cohn, A.G., Renz, J.: Qualitative spatial representation and reasoning. In: van Harmelen, F., Lifschitz, V., Porter, B. (eds.) Foundations of Artificial Intelligence. Handbook of Knowledge Representation, 1st edn., pp. 551–596. Elsevier (2008)Google Scholar
  6. Coventry, K.R., Garrod, S.: Towards a classification of extra-geometric influences on the comprehension of spatial prepositions. In: Carlson, L.A., van der Zee, E. (eds.) Functional Features in Language and Space. Oxford University Press (2004)Google Scholar
  7. Cowan, N.: The magical number 4 in short term memory. A reconsideration of storage capacity. Behavioral and Brain Sciences 24, 87–186 (2001)CrossRefGoogle Scholar
  8. Dylla, F., Wallgrün, J.O.: Qualitative spatial reasoning with conceptual neighborhoods for agent control. Journal of Intelligent and Robotic Systems 48(1), 55–78 (2007)CrossRefGoogle Scholar
  9. Egenhofer, M.J., Al-Taha, K.K.: Reasoning about Gradual Changes of Topological Relationships. In: Frank, A.U., Formentini, U., Campari, I. (eds.) GIS 1992. LNCS, vol. 639, pp. 196–219. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  10. Egenhofer, M.J., Franzosa, R.D.: Point-set topological spatial relations. International Journal of Geographical Information Systems 5(2), 161–174 (1991)CrossRefGoogle Scholar
  11. Freksa, C.: Temporal reasoning based on semi-intervals. Artificial Intelligence 54(1), 199–227 (1992)MathSciNetCrossRefGoogle Scholar
  12. Galton, A.: Qualitative spatial change. Spatial information systems. Oxford Univ. Press, Oxford (2000)Google Scholar
  13. Heil, M., Jansen-Osmann, P.: Sex differences in mental rotation with polygons of different complexity: Do men utilize holistic processes whereas women prefer piecemeal ones? The Quarterly Journal of Experimental Psychology 61(5) (2008)Google Scholar
  14. Klippel, A.: Spatial information theory meets spatial thinking - Is topology the Rosetta Stone of spatio-temporal cognition? Annals of the Association of American Geographers (67 manuscript pages) (accepted)Google Scholar
  15. Klippel, A., Li, R., Hardisty, F., Weaver, C.: Cognitive Invariants of Geographic Event Conceptualization: What Matters and What Refines? In: Fabrikant, S.I., Reichenbacher, T., van Kreveld, M., Schlieder, C. (eds.) GIScience 2010. LNCS, vol. 6292, pp. 130–144. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. Klippel, A., Li, R., Yang, J., Hardisty, F., Xu, S.: The Egenhofer-Cohn Hypothesis: Or, Topological Relativity? In: Raubal, M., Frank, A.U., Mark, D.M. (eds.) Cognitive and Linguistic Aspects of Geographic Space - New Perspectives on Geographic Information Research (in press)Google Scholar
  17. Knauff, M., Rauh, R., Renz, J.: A Cognitive Assessment of Topological Spatial Relations: Results from an Empirical Investigation. In: Frank, A.U. (ed.) COSIT 1997. LNCS, vol. 1329, pp. 193–206. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  18. Kos, A.J., Psenicka, C.: Measuring cluster similarity across methods. Psychological Reports 86, 858–862 (2000)CrossRefGoogle Scholar
  19. Kuhn, W.: An Image-Schematic Account of Spatial Categories. In: Winter, S., Duckham, M., Kulik, L., Kuipers, B. (eds.) COSIT 2007. LNCS, vol. 4736, pp. 152–168. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. Lakoff, G., Johnson, M.: Metaphors we live by. University of Chicago Press, Chicago (1980)Google Scholar
  21. Li, B., Fonseca, F.: TDD: A comprehensive model for qualitative spatial similarity assessment. Spatial Cognition and Computation 6(1), 31–62 (2006)CrossRefGoogle Scholar
  22. Mark, D.M.: Spatial representation: A cognitive view. In: Maguire, D.J., Goodchild, M.F., Rhind, D.W., Longley, P.A. (eds.) Geographical Information Systems: Principles and Applications, 2nd edn., vol. 1, pp. 81–89 (1999)Google Scholar
  23. Mark, D.M., Egenhofer, M.J.: Calibrating the meanings of spatial predi-cates from natural language: Line-region relations. In: Waugh, T.C., Healey, R.G. (eds.) Advances in GIS Research, pp. 538–553 (1994)Google Scholar
  24. Medin, D.L., Wattenmaker, W.D., Hampson, S.E.: Family resemblance, conceptual cohesiveness, and category construction. Cognitive Psychology 19(2) (1987)Google Scholar
  25. Papadias, D., Delis, V.: Relation-based similarity. In: Proceedings of the 5th ACM Workshop on GIS, Las Vegas, pp. 1–4. ACM (1997)Google Scholar
  26. Pothos, E.M., Close, J.: One or two dimensions in spontaneous classifica-tion: A simplicity approach. Cognition (2), 581–602 (2008)Google Scholar
  27. Randell, D.A., Cui, Z., Cohn, A.G.: A spatial logic based on regions and connections. In: Nebel, B., Rich, C., Swartout, W.R. (eds.) Proceedings of the 3rd International Conference on Knowledge Representation and Reasoning, pp. 165–176. Morgan Kaufmann, San Francisco (1992)Google Scholar
  28. Schwering, A.: Semantic similarity of natural language spatial relations. In: Conference on Artificial Intelligence and Simulation of Behaviour: Artificial and Ambient Intelligence. Symposium: Spatial Reasoning and Communication (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jan Oliver Wallgrün
    • 1
  • Jinlong Yang
    • 1
  • Alexander Klippel
    • 1
  • Frank Dylla
    • 2
  1. 1.Department of Geography, GeoVISTA CenterThe Pennsylvania State UniversityUSA
  2. 2.Cognitive Systems, Spatial Cognition SFB/TR 8Universität BremenGermany

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