SIM-DLA: A Novel Semantic Similarity Measure for Description Logics Reducing Inter-concept to Inter-instance Similarity

  • Krzysztof Janowicz
  • Marc Wilkes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5554)

Abstract

While semantic similarity plays a crucial role for human categorization and reasoning, computational similarity measures have also been applied to fields such as semantics-based information retrieval or ontology engineering. Several measures have been developed to compare concepts specified in various description logics. In most cases, these measures are either structural or require a populated ontology. Structural measures fail with an increasing expressivity of the used description logic, while several ontologies, e.g., geographic feature type ontologies, are not populated at all. In this paper, we present an approach to reduce inter-concept to inter-instance similarity and thereby avoid the canonization problem of structural measures. The novel approach, called SIM-DLA, reuses existing similarity functions such as co-occurrence or network measures from our previous SIM-DL measure. The required instances for comparison are derived from the completion tree of a slightly modified DL-tableau algorithm as used for satisfiability checking. Instead of trying to find one (clash-free) model, the new algorithm generates a set of proxy individuals used for comparison. The paper presents the algorithm, alignment matrix, and similarity functions as well as a detailed example.

References

  1. 1.
    Rissland, E.L.: AI and similarity. IEEE Intelligent Systems 21(3), 39–49 (2006)CrossRefGoogle Scholar
  2. 2.
    Janowicz, K.: Sim-DL: Towards a semantic similarity measurement theory for the description logic \(\cal A\!L\!C\!N\!R\) in geographic information retrieval. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops. LNCS, vol. 4278, pp. 1681–1692. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Janowicz, K., Keßler, C., Schwarz, M., Wilkes, M., Panov, I., Espeter, M., Baeumer, B.: Algorithm, implementation and application of the SIM-DL similarity server. In: Fonseca, F., Rodríguez, M.A., Levashkin, S. (eds.) GeoS 2007. LNCS, vol. 4853, pp. 128–145. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Janowicz, K., Keßler, C., Panov, I., Wilkes, M., Espeter, M., Schwarz, M.: A study on the cognitive plausibility of SIM-DL similarity rankings for geographic feature types. In: Bernard, L., Friis-Christensen, A., Pundt, H. (eds.) 11th AGILE International Conference on Geographic Information Science, Girona, Spain. Lecture Notes in Geoinformation and Cartography, pp. 115–133. Springer, Heidelberg (2008)Google Scholar
  5. 5.
    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
  6. 6.
    Medin, D., Goldstone, R., Gentner, D.: Respects for similarity. Psychological Review 100(2), 254–278 (1993)CrossRefGoogle Scholar
  7. 7.
    Cruz, I., Sunna, W.: Structural alignment methods with applications to geospatial ontologies. Transactions in GIS 12(6), 683–711 (2008)CrossRefGoogle Scholar
  8. 8.
    Euzenat, J., Valtchev, P.: Similarity-based ontology alignment in OWL-lite. In: de Mántaras, R.L., Saitta, L. (eds.) Proceedings of the 16th European Conference on Artificial Intelligence (ECAI 2004), pp. 333–337. IOS Press, Amsterdam (2004)Google Scholar
  9. 9.
    Shvaiko, P., Euzenat, J.: Ten challenges for ontology matching. In: Meersman, R., Tari, Z. (eds.) On the Move to Meaningful Internet Systems: OTM 2008. LNCS, vol. 5332, pp. 1164–1182. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Falconer, S., Noy, N., Storey, M.A.: Ontology mapping - a user survey. In: Shvaiko, P., Euzenat, J., Giunchiglia, F., He, B. (eds.) Proceedings of the Workshop on Ontology Matching (OM 2007) at ISWC/ASWC 2007, Busan, South Korea (2007)Google Scholar
  11. 11.
    Ricklefs, M., Blomqvist, E.: Ontology-based relevance assessment: An evaluation of different semantic similarity measures. In: Meersman, R., Tari, Z. (eds.) On the Move to Meaningful Internet Systems: OTM 2008. OTM Conferences (2). LNCS, vol. 5332, pp. 1235–1252. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Raubal, M.: Formalizing conceptual spaces. In: Varzi, A., Vieu, L. (eds.) Formal Ontology in Information Systems, Proceedings of the Third International Conference (FOIS 2004), vol. 114, pp. 153–164. IOS Press, Torino (2004)Google Scholar
  14. 14.
    Borgida, A., Walsh, T., Hirsh, H.: Towards measuring similarity in description logics. In: International Workshop on Description Logics (DL 2005), July 2005. CEUR Workshop Proceedings, vol. 147. CEUR, Edinburgh (2005)Google Scholar
  15. 15.
    d’Amato, C., Fanizzi, N., Esposito, F.: A semantic similarity measure for expressive description logics. In: CILC 2005, Convegno Italiano di Logica Computazionale, Rome, Italy (2005)Google Scholar
  16. 16.
    Araújo, R., Pinto, H.S.: Semilarity: Towards a model-driven approach to similarity. In: International Workshop on Description Logics (DL 2007), vol. 20, pp. 155–162. Bolzano University Press, Bozen-Bolzano (2007)Google Scholar
  17. 17.
    Albertoni, R., Martino, M.D.: Semantic similarity of ontology instances tailored on the application context. In: Meersman, R., Tari, Z. (eds.) OTM 2006. LNCS, vol. 4275, pp. 1020–1038. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Janowicz, K.: Kinds of contexts and their impact on semantic similarity measurement. In: Proceedings of the 6th IEEE International Conference on Pervasive Computing and Communications; 5th Workshop on Context Modeling and Reasoning (CoMoRea 2008), Hong Kong, pp. 441–446. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  19. 19.
    Keßler, C.: Similarity measurement in context. In: Kokinov, B., Richardson, D.C., Roth-Berghofer, T.R., Vieu, L. (eds.) CONTEXT 2007. LNCS, vol. 4635, pp. 277–290. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    d’Amato, C., Fanizzi, N., Esposito, F.: Query answering and ontology population: An inductive approach. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 288–302. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Goodman, N.: Seven strictures on similarity. In: Problems and projects, pp. 437–447. Bobbs-Merrill, New York (1972)Google Scholar
  22. 22.
    Ashby, F.G., Perrin, N.A.: Toward a unified theory of similarity and recognition. Psychological Review 95, 124–150 (1988)CrossRefGoogle Scholar
  23. 23.
    Cross, V., Sudkamp, T.: Similarity and Computability in Fuzzy Set Theory: Assessments and Applications. Studies in Fuzziness and Soft Computing, vol. 93. Physica-Verlag (2002)Google Scholar
  24. 24.
    d’Amato, C., Fanizzi, N., Esposito, F.: A dissimilarity measure for \(\cal A\!L\!C\) concept descriptions. In: SAC 2006: Proceedings of the 2006 ACM symposium on Applied computing, pp. 1695–1699. ACM Press, New York (2006)Google Scholar
  25. 25.
    Markman, A.B.: Structural alignment, similarity, and the internal structure of category representations. In: Similarity and Categorization, pp. 109–130. Oxford University Press, Oxford (2001)CrossRefGoogle Scholar
  26. 26.
    Egenhofer, M., Al-Taha, 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
  27. 27.
    Teege, G.: Making the difference: A subtraction operation for description logics. In: Doyle, J., Sandewall, E., Torasso, P. (eds.) 4th International Conference on Principles of Knowledge Representation and Reasoning (KR 1994), Bonn, Germany, pp. 540–550. Morgan Kaufmann, San Francisco (1994)CrossRefGoogle Scholar
  28. 28.
    Horrocks, I.: Implementation and Optimization Techniques. In: The Description Logic Handbook: Theory, Implementation and Applications, pp. 306–346. Cambridge University Press, Cambridge (2003)Google Scholar
  29. 29.
    Horrocks, I.: Optimising Tableaux Decision Procedures for Description Logics. PhD thesis, University of Manchester (1997)Google Scholar
  30. 30.
    Horrocks, I., Sattler, U., Tobies, S.: A description logic with transitive and converse roles, role hierarchies and qualifying number restrictions. LTCS-Report LTCS-99-08, LuFG Theoretical Computer Science, RWTH Aachen (1999)Google Scholar
  31. 31.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics 19, 17–30 (1989)CrossRefGoogle Scholar
  32. 32.
    Lutz, C., Möller, R.: Defined topological relations in description logics. In: Rousset, M.C., Brachman, R., Donini, F., Franconi, E., Horrocks, I., Levy, A. (eds.) Proceedings of the International Workshop on Description Logics, Gif sur Yvette, Paris, France, Université Paris-Sud, Centre d’Orsay, September 1997, pp. 15–19 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Krzysztof Janowicz
    • 1
  • Marc Wilkes
    • 1
  1. 1.Institute for GeoinformaticsUniversity of MuensterGermany

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