JACIII Vol.11 No.7 pp. 803-816
doi: 10.20965/jaciii.2007.p0803


Coincidence-Based Scoring of Mappings in Ontology Alignment

Seyed H. Haeri (Hossein), Hassan Abolhassani,
Vahed Qazvinian, and Babak Bagheri Hariri

Web Intelligence Laboratory, Computer Engineering Department, Sharif University of Technology and School of Computer Science, Institute for Studies in Theoretical Physics and Mathematics (IPM)

January 31, 2007
May 22, 2007
coincidence-based, ontology matching, metric spaces, genetic algorithms, graph theory

Ontology Matching (OM) which targets finding a set of alignments across two ontologies, is a key enabler for the success of Semantic Web. In this paper, we introduce a new perspective on this problem. By interpreting ontologies as Typed Graphs embedded in a Metric Space, coincidence of the structures of the two ontologies is formulated. Having such a formulation, we define a mechanism to score mappings. This scoring can then be used to extract a good alignment among a number of candidates. To do this, this paper introduces three approaches: The first one, straightforward and capable of finding the optimum alignment, investigates all possible alignments, but its runtime complexity limits its use to small ontologies only. To overcome this shortcoming, we introduce a second solution as well which employs a Genetic Algorithm (GA) and shows a good effectiveness for some certain test collections. Based on approximative approaches, a third solution is also provided which, for the same purpose, measures random walks in each ontology versus the other.

  1. [1] J. Euzenat, “Towards composing and benchmarking ontology alignments,” [Online].
  2. [2] J. Euzenat et al., “State of the art on ontology alignment,” Knowledge web NoE, deliverable 2.2.3, 2004.
  3. [3] P. Bouquet, J. Euzenat, E. Franconi, L. Serafini, G. Stamou, and S. Tessaris, “Specification of a common framework for characterizing alignment,” Knowledge web NoE, deliverable 2.2.1, 2004.
  4. [4] A. Doan, J. Madhavan, P. Domingos, and A. Halevy, “Ontology matching: A machine learning approach,” Handbook on Ontologies in Information Systems, Springer-Verlag, 2003.
  5. [5] P. Shvaiko and J. Euzenat, “A survey of schema-based matching approaches,” Journal on Data Semantics, Vol.IV, 2005.
  6. [6] “Call for papers – 4th european semantic web conference,” 2007, [Online].
  7. [7] J. Hopcroft and R. Karp, “An n5/2 algorithm for maximum matchings in bipartite graphs,” SIAM Journal on Computing, Vol.2, No.4, pp. 225-231, 1973.
  8. [8] C. H. Papadimitriou and K. Steiglitz, “Combinatorial Optimization Algorithms and Complexity,” Prentice-Hall, 1998.
  9. [9] E. Rahm and P. Bernstein, “A survey of approaches to automatic schema matching,” VLDB Journal, Vol.10, No.4, pp. 334-350, 2001.
  10. [10] G. Bisson, “Learning in fol with similarity measure,” in Proceedings of the 10th American Association for Artificial Intelligence conference, San-Jose (CA US), pp. 82-87, 1992.
  11. [11] G. Schreiber, R. de Hoog, H. Akkermans, A. Anjewierden, N. Shadbolt, and W. V. de Velde, “Knowledge Engineering and Management,” MIT Press, 2000.
  12. [12] “Ontoweb. a survey on ontology tools. eu thematic network, ist-2000- 29243 deliverable 1.3, ontoweb – ontology-based information exchange for knowledge management and electronic commerce,”
    available online:, May 2002.
  13. [13] P. Valtchev, “Construction automatique de taxonomies pour laide la reprsentation de connaissances par objets,” Ph.D. Dissertation, Universite Grenoble, 1999.
  14. [14] A. Maedche and V. Zacharias, “Clustering ontologybased metadata in the semantic web,” in Proceedings of the 13th ECML and 6th PKDD, 2002.
  15. [15] P. Resnik, “Using information content to evaluate semantic similarity in a taxonomy,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95), 1995.
  16. [16] M. Ehrig and S. Staab, “Qom – quick ontology mapping,” in Proc. ISWC-2003., 2003.
  17. [17] M. Ehrig and Y. Sure, “Ontology mapping – an integrated approach,” in 1st European Semantic Web Symposium (ESWS), pp. 76-91, 2004.
  18. [18] J. Euzenat, J. Barrasa, P. Bouquet, and J. Bo, “State of the art on ontology alignment,” Knowledge Web, Statistical Research Division, 2004.
  19. [19] A. Doan, P. Domingos, and A. Halevy, “Learning to match the schemas of data sources: A multistrategy approach,” Machine Learning, Vol.50, No.3, pp. 279-301, 2003.
  20. [20] Y. S. M. Ehrig and S. Staab, “Bootstrapping ontology alignment methods with apfel,” in Proceedings of the 4th International Semantic Web Conference (ISWC-2005), ser. Lecture Notes in Computer Science, pp. 186-200, 2005.
  21. [21] H. Abolhassani, S. H. Haeri, and B. Hariri, “On ontology alignment experiments,” Webology, Vol.3, No.3, 2006.
  22. [22] R. Dieng and S. Hug, “Comparison of ‘personal ontologies’ represented through conceptual graphs,” in 13th ECAI, Brighton (UK), pp. 341-345, 1998.
  23. [23] S. Staab and A. Maedche, “Measuring similarity between ontologies,” Lecture notes in artificial intelligence, No.2473, pp. 251-263, 2002.
  24. [24] G. Stumme and A. Maedche, “Fca-merge: bottom-up merging of ontologies,” in In Proc. 17th IJCAI, Seattle (WA US), pp. 225-230, 2001.
  25. [25] A. V. Zhdanova and P. Shvaiko, “Community-driven ontology matching,” in Proc. of ESWC, pp. 34-49, 2006.
  26. [26] S. Melnik, H. Garcia-Molina, and E. Rahm, “Similarity flooding: a versatile graph matching algorithm,” in Proc. 18th International Conference on Data Engineering (ICDE), San Jose (CA US), pp. 117-128, 2002.
  27. [27] A. Gibbons, “Algorithmic Graph Theory,” Cambridge University Press, 1985.
  28. [28] P. Mitra, N. F. Noy, and A. R. Jaiswal, “Omen: A probabilistic ontology mapping tool,” in 4th international semantic web conference (ISWC 2005), Vol.3729, pp. 537-547, 2003.
  29. [29] H. L. Johnson, K. B. Cohen, W. A. Baumgartner, Z. Lu, M. Bada, T. Kester, H. Kim, and L. Hunter, “Evaluation of lexical methods for detecting relationships between concepts from multiple ontologies,” Pac Symp Biocomput, pp. 28-39, 2006.
  30. [30] J. Wang and L. Gasser, “Mutual online ontology alignment,” in Proc. of the AAMAS 2002 Workshop, 2002.
  31. [31] J. Li, “Lom: A lexicon-based ontology mapping tool,” in Proceeding of the Performance Metrics for Intelligent Systems (Per-MIS’04), Information Interpretation and Integration Conference (I3CON), Gaithersburg, MD., 2004.
  32. [32] W. Rudin, “Principles of Mathematical Analysis,” 3rd ed., New York: McGraw-Hill, 1976.
  33. [33] S. Abramsky, “Domain Theory in Logical Form,” 1987.
  34. [34] R. Kothari, J. Basak, and I. Block, “Perceptually motivated measures for capturing proximity of web page elements: Towards automated evaluation of web page layouts,” in The 11th International World Wide Web Conference, 2002.
  35. [35] “Webster’s New World College Dictionary,” 4th ed., New York: Macmillan, 1998.
  36. [36] W. A. Wilson, “On quasi-metric spaces,” American Journal of Mathematics, Vol.43, pp. 675-684, 1931.
  37. [37] R. Baeza-Yates and B. Ribeiro-Neto, “Modern Information Retrieval,” Addison-Wesley, 1999, bAE r2 99:1 1.Ex.
  38. [38] W3C, “Owl web ontology language guide, w3c recommendation 10 february 2004,” Tech. Rep., 2004, [Online].
  39. [39] V. Qazvinian, H. Abolhassani, and S. H. Haeri, “Coincidence based mapping extraction with genetic algorithms,” in Proceedings of 3rd International Conference on Web Information Systems and Technologies (Webist 2007) Barcelona, Spain, March, 2007.
  40. [40] V. I. Levenshtein, “Binary codes capable of correcting deletions, insertions and reversals,” Sov. Phys. Dokl., Vol.6, pp. 707-710, 1966.
  41. [41] “Tourism ontology FOAM,”
    available online:
  42. [42] “Call for papers – evaluation of ontology-based tools, 3rd international workshop,” 2004, [Online].
  43. [43] EON2004, “EON ontology alignment contest,” 2004, [Online]. Available:
  44. [44] “Evaluation of ontology-based tools, 3rd international workshop, table of results,” 2004, [Online].
  45. [45] F.Malucelli, T. Ottmann, and D. Pretolani, “Efficient labelling algorithm for the maximum non crossing matching problem,” Discrete Applied Mathematics, Vol.47, pp. 175-179, 1993.
  46. [46] D. West, “Introduction to Graph Theory (2nd ed.),” Upper Saddle River: Prentice Hall, 2001.
  47. [47] S. H. Haeri, B. B. Hariri, and H. Abolhassani, “Coincidence-based refinement of ontology matching,” in Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on advanced Intelligent Systems, 2006.
  48. [48] B. Xiao, H. Yu, and E. Hancock, “Graph matching using spectral embedding and semidefinite programming,” in Proceedings of the 15th British Machine Vision Conference, 2004.
  49. [49] R. Tennent, “The denotational semantics of programming languages,” Communications of the ACM, Vol.19, p. 437, 1976.

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