Advertisement

SeeCOnt: A New Seeding-Based Clustering Approach for Ontology Matching

  • Alsayed AlgergawyEmail author
  • Samira Babalou
  • Mohammad J. Kargar
  • S. Hashem Davarpanah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9282)

Abstract

Ontology matching plays a crucial role to resolve semantic heterogeneities within knowledge-based systems. However, ontologies contain a massive number of concepts, resulting in performance impediments during the ontology matching process. With the increasing number of ontology concepts, there is a growing need to focus more on large-scale matching problems. To this end, in this paper, we come up with a new partitioning-based matching approach, where a new clustering method for partitioning concepts of ontologies is introduced. The proposed method, called SeeCOnt, is a seeding-based clustering technique aiming to reduce the complexity of comparison by only using clusters’ seed. In particular, SeeCOnt first identifies and determines the seeds of clusters based on the highest ranked concepts using a distribution condition, then the remaining concepts are placed into the proper cluster by defining and utilizing a membership function. The SeeCOnt method can improve the memory consuming problem in the large-scale matching problem, as well as it increases the matching quality. The experimental evaluation shows that SeeCOnt, compared with the top ten participant systems in OAEI, demonstrates acceptable results.

Keywords

Ontology matching Clustering techniques Large-scale matching 

Notes

Acknowledgments

A. Algergawy’work is partly funded by DFG in the INFRA1 project of CRC AquaDiva.

References

  1. 1.
    Algergawy, A., Massmann, S., Rahm, E.: A clustering-based approach for large-scale ontology matching. In: Eder, J., Bielikova, M., Tjoa, A.M. (eds.) ADBIS 2011. LNCS, vol. 6909, pp. 415–428. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  2. 2.
    Algergawy, A., Nayak, R., Saake, G.: Element similarity measures in XML schema matching. Inf. Sci. 180(24), 4975–4998 (2010)CrossRefGoogle Scholar
  3. 3.
    Bellahsene, Z., Bonifati, A., Rahm, E.: Schema Matching and Mapping. Springer, Heidelberg (2011) CrossRefzbMATHGoogle Scholar
  4. 4.
    Do, H.H., Rahm, E.: Matching large schemas: approaches and evaluation. Inf. Syst. 32(6), 857–885 (2007)CrossRefGoogle Scholar
  5. 5.
    Doan, A., Halevy, A.: Semantic integration research in the database community: A brief survey. AAAI AI Mag. 25(1), 83–94 (2005)Google Scholar
  6. 6.
    Doan, A., Halevy, A.Y., Ives, Z.G.: Principles of Data Integration. Morgan Kaufmann, USA (2012) Google Scholar
  7. 7.
    Ehrig, M., Staab, S.: QOM – quick ontology mapping. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 683–697. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  8. 8.
    Euzenat, J., Shvaiko, P.: Ontology Matching, 2nd edn. Springer, Heidelberg (2013) CrossRefzbMATHGoogle Scholar
  9. 9.
    Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1997)CrossRefGoogle Scholar
  11. 11.
    Graves, A., Adali, S., Hendler, J.: A method to rank nodes in an RDF graph. In: 7th International Semantic Web Conference (Posters and Demos) (2008)Google Scholar
  12. 12.
    Hage, P., Harary, F.: Eccentricity and centrality in networks. Soc. Netw. 17, 57–63 (1995)CrossRefGoogle Scholar
  13. 13.
    Hamdi, F., Safar, B., Reynaud, C., Zargayouna, H.: Alignment-based partitioning of large-scale ontologies. In: Guillet, F., Ritschard, G., Zighed, D.A., Briand, H. (eds.) Advances in Knowledge Discovery and Management. SCI, vol. 292, pp. 251–269. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  14. 14.
    Hendler, J.: Agents and the semantic web. IEEE Intell. Syst. J. 16, 30–37 (2001)CrossRefGoogle Scholar
  15. 15.
    Hu, W., Qu, Y., Cheng, G.: Matching large ontologies: A divide-and-conquer approach. DKE 67, 140–160 (2008)CrossRefGoogle Scholar
  16. 16.
    Kermarrec, A.-M., Merrer, E.L., Sericola, B., Trdan, G.: Second order centrality: Distributed assessment of nodes criticity in complex networks. Comput. Commun. 34, 619–628 (2011)CrossRefGoogle Scholar
  17. 17.
    Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O.: Centrality indices. In: Brandes, U., Erlebach, T. (eds.) Network Analysis. LNCS, vol. 3418, pp. 16–61. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  18. 18.
    Rahm, E.: Towards large-scale schema and ontology matching. In: Bellahsene, Z., Bonifati, A., Rahm, E. (eds.) Data-Centric Systems and Applications, vol. 5258, pp. 3–27. Springer, Heidelberg (2011)Google Scholar
  19. 19.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J. 10(4), 334–350 (2001)CrossRefzbMATHGoogle Scholar
  20. 20.
    Seddiquia, M.H., Aono, M.: An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size. Web Semant. 7(4), 344–356 (2009)CrossRefGoogle Scholar
  21. 21.
    Shvaiko, P., Euzenat, J.: Ontology matching: State of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)CrossRefGoogle Scholar
  22. 22.
    Shvaiko, P., Euzenat, J., Mao, M., Jimnez-Ruiz, E., Li, J., Ngonga, A.: editors. 9th International Workshop on Ontology Matching collocated with the 13th International Semantic Web Conference (ISWC 2014) (2014)Google Scholar
  23. 23.
    Wang, Z., Wang, Y., Zhang, S.-S., Shen, G., Du, T.: Matching large scale ontology effectively. In: Mizoguchi, R., Shi, Z.-Z., Giunchiglia, F. (eds.) ASWC 2006. LNCS, vol. 4185, pp. 99–105. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  24. 24.
    Hu, W., Zhao, Y., Qu, Y.: Partition-based block matching of large class hierarchies. In: Mizoguchi, R., Shi, Z.-Z., Giunchiglia, F. (eds.) ASWC 2006. LNCS, vol. 4185, pp. 72–83. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  25. 25.
    Zhong, Q., Li, H., Li, J., Xie, G.T., Tang, J., Zhou, L., Pan, Y.: A Gauss function based approach for unbalanced ontology matching. In: the ACM SIGMOD International Conference on Management of Data, (SIGMOD 2009), pp. 669–680 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alsayed Algergawy
    • 1
    • 2
    Email author
  • Samira Babalou
    • 3
  • Mohammad J. Kargar
    • 3
  • S. Hashem Davarpanah
    • 3
  1. 1.Institute of Computer ScienceFriedrich Schiller University of JenaJenaGermany
  2. 2.Department of Computer EngineeringTanta UniversityTantaEgypt
  3. 3.Department of Computer EngineeringUniversity of Science and CultureTehranIran

Personalised recommendations