Knowledge and Information Systems

, Volume 26, Issue 2, pp 225–247 | Cite as

Evaluation of two heuristic approaches to solve the ontology meta-matching problem

  • Jorge Martinez-Gil
  • José F. Aldana-Montes
Regular Paper


Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies. Ontology meta-matching, which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the ontology meta-matching problem. The first one is called maximum similarity measure, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called genetics for ontology alignments and is based on a genetic algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.


Ontology meta-matching Knowledge management Information integration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aumueller D, Do HH, Massmann S, Rahm E (2005) Schema and ontology matching with COMA++. In: SIGMOD conference 2005, pp 906–908Google Scholar
  2. 2.
    Baeza-Yates R, Ribeiro-Neto BA (1999) Modern information retrieval. ACM Press, New York ISBN 0-201-39829-XGoogle Scholar
  3. 3.
    Berners-Lee T, Hendler J, Lassila O (2001) The semantic Web. Scientific American, HarlanGoogle Scholar
  4. 4.
    Buckland MK, Gey FC (1994) The relationship between recall and precision. JASIS 45(1): 12–19CrossRefGoogle Scholar
  5. 5.
    Cabral L, Domingue J, Motta E, Payne TR, Hakimpour F (2004) Approaches to semantic Web services: an overview and comparisons. In: ESWS 2004, pp 225–239Google Scholar
  6. 6.
    Chen H, Perich F, Finin TW, Joshi A (2004) SOUPA: standard ontology for ubiquitous and pervasive applications. MobiQuitous, pp 258–267Google Scholar
  7. 7.
    Chortaras A, Stamou GB, Stafylopatis A (2005) Learning ontology alignments using recursive neural networks. In: ICANN (2) 2005, pp 811–816Google Scholar
  8. 8.
    Cilibrasi R, Vitanyi PMB (2007) The Google similarity distance. IEEE Trans Knowl Data Eng 19(3): 370–383CrossRefGoogle Scholar
  9. 9.
    Cohen GD, Litsyn S, ZémorGOn greedy algorithms in coding theory. IEEE Trans Inf heory 42(6):2053–2057Google Scholar
  10. 10.
    Dietz JLG (2005) Enterprise ontology. In: ICEIS 2005, vol 1, p 5Google Scholar
  11. 11.
    Do HH, Rahm E (2002) COMA—a system for flexible combination of schema matching approaches. In: VLDB 2002, pp 610–621Google Scholar
  12. 12.
    Doerr M (2001) Semantic problems of Thesaurus mapping. J. Dig. Inf. 1(8)Google Scholar
  13. 13.
    Domshlak C, Gal A, Roitman H (2007) Rank aggregation for automatic schema matching. IEEE Trans Knowl Data Eng 19(4): 538–553CrossRefGoogle Scholar
  14. 14.
    Drumm C, Schmitt M, Do HH, Rahm E (2007) Quickmig: automatic schema matching for data migration projects. In CIKM 2007, pp 107–116Google Scholar
  15. 15.
    Ehrig M, Staab S, Sure Y (2005) Bootstrapping ontology alignment methods with APFEL. In: International semantic Web conference 2005, pp 186–200Google Scholar
  16. 16.
    Ehrig M, Sure Y (2005) FOAM—framework for ontology alignment and mapping—results of the ontology alignment evaluation initiative. Integr. Ontol.Google Scholar
  17. 17.
    Ehrig M (2007) Ontology alignment: bridging the semantic gap (contents). Springer, Berlin. ISBN 978-0-387-36501-5Google Scholar
  18. 18.
    Euzenat J, Shvaiko P (2007) Ontology matching. Springer, BerlinzbMATHGoogle Scholar
  19. 19.
    Falconer S, Noy N (2007) Ontology mapping—a user survey. In: The second international workshop on ontology matching. ISWC/ASWC, pp 49–60Google Scholar
  20. 20.
    Forrest S (1997) Genetic algorithms. The computer science and engineering handbook, pp 557–571Google Scholar
  21. 21.
    Gal A, Anaby-Tavor A, Trombetta A, Montesi D (2005) A framework for modeling and evaluating automatic semantic reconciliation. VLDB J 14(1): 50–67CrossRefGoogle Scholar
  22. 22.
    Giunchiglia F, Shvaiko P, Yatskevich M (2004) S-Match: an algorithm and an implementation of semantic matching. In: ESWS 2004, pp 61–75Google Scholar
  23. 23.
    He B, Chang KCC (2005) Making holistic schema matching robust: an ensemble approach. In: KDD 2005, pp 429–438Google Scholar
  24. 24.
    Hu W, Cheng G, Zheng D, Zhong X, Qu Y (2006) The results of Falcon-AO in the OAEI 2006 campaign. Ontol MatchingGoogle Scholar
  25. 25.
    Huang J, Dang J, Vidal JM, Huhns MN (2007) Ontology matching using an artificial neural network to learn weights. In: IJCAI workshop on semantic Web for collaborative knowledge acquisitionGoogle Scholar
  26. 26.
    Ji Q, Liu W, Qi G, Bell DA (2006) LCS: a linguistic combination system for ontology matching. In: KSEM 2006, pp 176–189Google Scholar
  27. 27.
    Jordan MI, Bishop CM (1997) Neural networks. The computer science and engineering handbook, pp 536–556Google Scholar
  28. 28.
    Kiefer C, Bernstein A, Stocker M (2007) The fundamentals of iSPARQL: a virtual triple approach for similarity-based semantic Web tasks. In: ISWC/ASWC 2007, pp 295–309Google Scholar
  29. 29.
    Lambrix P, Tan H (2007) A tool for evaluating ontology alignment strategies. J Data Semant 8: 182–202Google Scholar
  30. 30.
    Langley P (1994) Elements of machine learning. ISBN 1-55860-301-8Google Scholar
  31. 31.
    Lee Y, Sayyadian M, Doan A, Rosenthal A (2001) eTuner: tuning schema matching software using synthetic scenarios. VLDB J 16(1): 97–122Google Scholar
  32. 32.
    Levenshtein V (1966) Binary codes capable of correcting deletions, insertions and reversals. Soviet Phys Doklady 10: 707–710MathSciNetGoogle Scholar
  33. 33.
    Li Y, Li JZ, Zhang D, Tang J (2006) Result of ontology alignment with RiMOM at OAEI’06. Ontol MatchingGoogle Scholar
  34. 34.
    Maedche A, Motik B, Silva N, Volz R (2002) MAFRA—a MApping FRAmework for distributed ontologies. In EKAW 2002, pp 235–250Google Scholar
  35. 35.
    Madhavan J, Bernstein PA, Rahm E (2001) Generic schema matching with cupid. In: VLDB 2001, pp 49–58Google Scholar
  36. 36.
    Maguitman A, Menczer F, Erdinc F, Roinestad H, Vespignani A (2006) Algorithmic computation and approximation of semantic similarity. World Wide Web 9(4): 431–456CrossRefGoogle Scholar
  37. 37.
    Martinez-Gil J, Alba E, Aldana-Montes JF (2008) Optimizing ontology alignments by using genetic algorithms. In: Proceedings of the workshop on nature based reasoning for the semantic Web. Karlsruhe, GermanyGoogle Scholar
  38. 38.
    Martinez-Gil J, Navas-Delgado I, Aldana-Montes JF (2008) SIFO. An efficient taxonomical matcher for ontology alignment. Technical Report ITI-08-3. Department of Languages and Computing Sciences, University of MalagaGoogle Scholar
  39. 39.
    Martinez-Gil J, Navas-Delgado I, Polo-Marquez A, Aldana-Montes JF (2008) Comparison of textual renderings of ontologies for improving their alignment. In: Proceedings of the second international conference on complex, intelligent and software intensive systems. Barcelona, Spain, pp 871–876Google Scholar
  40. 40.
    Melnik S, Garcia-Molina H, Rahm E (2002) Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: Proceedings of international conference on data engineering, pp 117–128Google Scholar
  41. 41.
    Mochol M, Bontas-Simperl EP (2006) A high-level architecture of a metadata-based ontology matching framework. In: DEXA workshops, pp 354–358Google Scholar
  42. 42.
    Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv 33(1): 31–88CrossRefGoogle Scholar
  43. 43.
    Niedbala S (2006) OWL-CtxMatch in the OAEI 2006 alignment contest. Ontol MatchingGoogle Scholar
  44. 44.
    Pappa GL, Freitas AA (2009) Evolving rule induction algorithms with multi-objective grammar-based genetic programming. Knowl Inf Syst 19(3): 283–309CrossRefGoogle Scholar
  45. 45.
    Pfitzner D, Leibbrandt R, Powers D (2009) Characterization and evaluation of similarity measures for pairs of clusterings. Knowl Inf Syst 19(3): 361–394CrossRefGoogle Scholar
  46. 46.
    Roitman H, Gal A (2006) OntoBuilder: fully automatic extraction and consolidation of ontologies from Web sources using sequence semantics. In: EDBT workshops 2006, pp 573–576Google Scholar
  47. 47.
    Rosenfeld B, Feldman R (2009) Self-supervised relation extraction from the Web. Knowl Inf Syst 17(1): 17–33CrossRefGoogle Scholar
  48. 48.
    Salton G, Buckley C (1990) Improving retrieval performance by relevance feedback. JASIS 41(4): 288–297CrossRefGoogle Scholar
  49. 49.
    Stoilos G, Stamou GB, Kollias SD (2005) A string metric for ontology alignment. In: Proceedings of international semantic Web conference 2005, pp 624–637Google Scholar
  50. 50.
    Kewei T, Yong Y: CMC: combining multiple schema-matching strategies based on credibility prediction. In: DASFAA 2005, pp 888–893Google Scholar
  51. 51.
    Ukkonen E (1992) Approximate string matching with q-grams and maximal matches. Theor Comput Sci 92(1): 191–211zbMATHCrossRefMathSciNetGoogle Scholar
  52. 52.
    Wang J, Ding Z, Jiang C (2006) GAOM: genetic algorithm based ontology matching. In: Proceedings of IEEE Asia–Pacific conference on services computing, pp 888–893Google Scholar
  53. 53.
    Wang P, Hu J, Zeng HJ, Chen Z (2009) Using Wikipedia knowledge to improve text classification. Knowl Inf Syst 19(3): 265–281CrossRefGoogle Scholar
  54. 54.
    Widdows D (2004) Geometry and meaning. The University of Chicago Press, ChicagozbMATHGoogle Scholar
  55. 55.
    Woon WL, Wong KD (2009) String alignment for automated document versioning. Knowl Inf Syst 18(3): 293–309CrossRefGoogle Scholar
  56. 56.
  57. 57.
    Ziegler P, Kiefer C, Sturm C, Dittrich KR, Bernstein A (2006) Detecting similarities in ontologies with the SOQA-SimPack Toolkit. In: Proceedings of EDBT 2006, pp 59–76Google Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.Department of Computer Languages and Computing SciencesUniversity of MálagaMalagaSpain

Personalised recommendations