Semantic Referencing – Determining Context Weights for Similarity Measurement

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6292)


Semantic similarity measurement is a key methodology in various domains ranging from cognitive science to geographic information retrieval on the Web. Meaningful notions of similarity, however, cannot be determined without taking additional contextual information into account. One way to make similarity measures context-aware is by introducing weights for specific characteristics. Existing approaches to automatically determine such weights are rather limited or require application specific adjustments. In the past, the possibility to tweak similarity theories until they fit a specific use case has been one of the major criticisms for their evaluation. In this work, we propose a novel approach to semi-automatically adapt similarity theories to the user’s needs and hence make them context-aware. Our methodology is inspired by the process of georeferencing images in which known control points between the image and geographic space are used to compute a suitable transformation. We propose to semi-automatically calibrate weights to compute inter-instance and inter-concept similarities by allowing the user to adjust pre-computed similarity rankings. These known control similarities are then used to reference other similarity values.


Semantic Similarity Geo-Semantics Information Retrieval 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goldstone, R.L., Son, J.: Similarity. In: Holyoak, K., Morrison, R. (eds.) Cambridge Handbook of Thinking and Reasoning, pp. 13–36. Cambridge University Press, Cambridge (2005)Google Scholar
  2. 2.
    Rissland, E.L.: Ai and similarity. IEEE Intelligent Systems 21(3), 39–49 (2006)CrossRefGoogle Scholar
  3. 3.
    Hofstadter, D.R.: Gödel, Escher, Bach: An Eternal Golden Braid. Basic Books, New York (1999)zbMATHGoogle Scholar
  4. 4.
    Gärdenfors, P.: Conceptual Spaces - The Geometry of Thought. Bradford Books. MIT Press, Cambridge (2000)Google Scholar
  5. 5.
    Nedas, K., Egenhofer, M.: Spatial similarity queries with logical operators. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. LNCS, vol. 2750, pp. 430–448. Springer, Heidelberg (2003)Google Scholar
  6. 6.
    Li, B., Fonseca, F.: Tdd - a comprehensive model for qualitative spatial similarity assessment. Spatial Cognition and Computation 6(1), 31–62 (2006)CrossRefGoogle Scholar
  7. 7.
    Raubal, M.: Formalizing conceptual spaces. In: Varzi, A., Vieu, L. (eds.) Formal Ontology in Information Systems, Proceedings of the Third International Conference (FOIS 2004), Torino, Italy, November 2004. Frontiers in Artificial Intelligence and Applications, vol. 114, pp. 153–164. IOS Press, Amsterdam (2004)Google Scholar
  8. 8.
    Janowicz, K., Schwarz, M., Wilkes, M.: Implementation and evaluation of a semantics-based user interface for web gazetteers. In: Workshop on Visual Interfaces to the Social and the Semantic Web, VISSW 2009 (2009)Google Scholar
  9. 9.
    Ahlqvist, O.: Extending post classification change detection using semantic similarity metrics to overcome class heterogeneity: a study of 1992 and 2001 national land cover database changes. Remote Sensing of Environment 112(3), 1226–1241 (2008)CrossRefGoogle Scholar
  10. 10.
    Egenhofer, M.: Toward the semantic geospatial web. In: GIS 2002: Proceedings of the 10th ACM international symposium on Advances in geographic information systems, pp. 1–4. ACM, New York (2002)CrossRefGoogle Scholar
  11. 11.
    Rodríguez, A., Egenhofer, M.: Comparing geospatial entity classes: an asymmetric and context-dependent similarity measure. International Journal of Geographical Information Science 18(3), 229–256 (2004)CrossRefGoogle Scholar
  12. 12.
    Janowicz, K., Wilkes, M.: SIM − DL A: A Novel Semantic Similarity Measure for Description Logics Reducing Inter-concept to Inter-instance Similarity. In: Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P., Heath, T., Hyvönen, E., Mizoguchi, R., Oren, E., Sabou, M., Simperl, E. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 353–367. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Cruz, I., Sunna, W.: Structural alignment methods with applications to geospatial ontologies. Transactions in GIS 12(6), 683–711 (2008)CrossRefGoogle Scholar
  14. 14.
    Adams, B., Raubal, M.: A metric conceptual space algebra. In: Hornsby, K.S., Claramunt, C., Denis, M., Ligozat, G. (eds.) COSIT 2009. LNCS, vol. 5756, pp. 51–68. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Goldstone, R.L., Medin, D.L., Halberstadt, J.: Similarity in context. Memory & Cognition 25, 237–255 (1997)Google Scholar
  16. 16.
    Janowicz, K.: Kinds of contexts and their impact on semantic similarity measurement. In: 5th IEEE Workshop on Context Modeling and Reasoning (CoMoRea 2008) at the 6th IEEE International Conference on Pervasive Computing and Communication (PerCom 2008), pp. 441–446 (2008)Google Scholar
  17. 17.
    Keßler, C.: What’s the difference? - a cognitive dissimilarity measure for information retrieval result sets. Knowledge and Information Systems (forthcoming)Google Scholar
  18. 18.
    Goldstone, R.L.: The role of similarity in categorization: providing a groundwork. Cognition 52(2), 125–157 (1994)CrossRefGoogle Scholar
  19. 19.
    Tversky, A.: Features of similarity. Psychological Review 84(4), 327–352 (1977)CrossRefGoogle Scholar
  20. 20.
    Dominich, S.: The Modern Algebra of Information Retrieval. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  21. 21.
    Keßler, C., Raubal, M., Wosniok, C.: Semantic rules for context-aware geographical information retrieval. In: Barnaghi, P., Moessner, K., Presser, M., Meissner, S. (eds.) EuroSSC 2009. LNCS, vol. 5741, pp. 77–92. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  22. 22.
    Schwering, A.: Approaches to semantic similarity measurement for geo-spatial data - a survey. Transactions in GIS 12(1), 5–29 (2008)CrossRefGoogle Scholar
  23. 23.
    Schwering, A., Raubal, M.: Spatial relations for semantic similarity measurement. In: Akoka, J., Liddle, S.W., Song, I.-Y., Bertolotto, M., Comyn-Wattiau, I., van den Heuvel, W.-J., Kolp, M., Trujillo, J., Kop, C., Mayr, H.C. (eds.) ER Workshops 2005. LNCS, vol. 3770, pp. 259–269. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    Navarro, D., Lee, M.: Combining dimensions and features in similarity-based representations. In: Becker, S., S.T., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, vol. 15, pp. 59–66. MIT Press, Cambridge (2003)Google Scholar
  25. 25.
    Gati, I., Tversky, A.: Representations of qualitative and quantitative dimensions. Journal of Experimental Psychology: Human Perception and Performance 8(2), 325–340 (1982)CrossRefGoogle Scholar
  26. 26.
    Gangemi, A.: Ontology design patterns for semantic web content. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 262–276. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  27. 27.
    Scheider, S., Probst, F., Janowicz, K.: Constructing bodies and their qualities from observations. In: 6th International Conference on Formal Ontology in Information Systems (FOIS 2010 forthcoming)Google Scholar
  28. 28.
    Ahlqvist, O.: In search for classification that support the dynamics of science? the fao land cover classification system and proposed modifications. Environment and Planning B: Planning and Design 35(1), 169–186 (2008)CrossRefGoogle Scholar
  29. 29.
    Janowicz, K., Maué, P., Wilkes, M., Braun, M., Schade, S., Dupke, S., Kuhn, W.: Similarity as a quality indicator in ontology engineering. In: Eschenbach, C., Grüninger, M. (eds.) 5th International Conference on Formal Ontology in Information Systems, October 2008, vol. 183, pp. 92–105. IOS Press, Amsterdam (2008)Google Scholar
  30. 30.
    Kraus, K.: Photogrammetry: Geometry from Images and Laser Scans, 2nd edn. Walter de Gruyter, Berlin (2007)Google Scholar
  31. 31.
    Medin, D., Goldstone, R., Gentner, D.: Respects for similarity. Psychological Review 100(2), 254–278 (1993)CrossRefGoogle Scholar
  32. 32.
    Gärdenfors, P., Williams, M.A.: Reasoning about categories in conceptual spaces. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI 2001), pp. 385–392 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of GeographyThe Pennsylvania State UniversityUSA
  2. 2.Department of Computer ScienceUniversity of CaliforniaSanta BarbaraUSA
  3. 3.Department of GeographyUniversity of CaliforniaSanta BarbaraUSA

Personalised recommendations