CSI Transactions on ICT

, Volume 5, Issue 3, pp 265–279 | Cite as

Parallelized and distributed task based ontology matching in clustering environment with semantic verification

  • Tanni Mittra
  • Muhammad Masroor Ali
Original Research


Recent advances in information and communication technology make huge amount of heterogeneous information available for us. But integration of information semantically and provide machine understandable meaning to information is still a great challenge in current web technology. In overcoming the challenges, ontology matching plays a vital role, which is introduced by semantic web technology. In this paper, we propose a new method of ontology matching using parallelization and distribution technique. To apply parallelism, we develop a partitioning algorithm by using property-by-class and subclass of relationship, which partitions the ontology into smaller clusters. Then the clusters from different ontologies are matched based on terminological and structural similarity with semantic verification. All these tasks of matching are handled in a parallel way and all the tasks are distributed over the computational resources. Thus, we significantly reduce the time complexity and space complexity of large scale matching task. Our proposed method reduces misaligned pairs while increasing correct aligned concepts. Validity of our claims have been substantiated through different experiments on small and large ontologies.


Ontology Ontology matching Data integration Semantic heterogeneity 



This work is a postgraduate research project of Bangladesh University of Engineering and Technology. We thank Wei Hu, Yuzhong Qu and Gong Cheng for providing Russia12 and TourismAB dataset. We avail this opportunity to thank our anonymous reviewers whose comments contributed in improving the quality of the paper.


  1. 1.
    Algergawy A, Massmann S, Rahm E (2011) A clustering-based approach for large-scale ontology matching. In: Eder J, Bieliková M, Tjoa AM (eds) East European Conference on Advances in Databases and Information Systems, Springer, Vienna, Austria, pp 415–428CrossRefGoogle Scholar
  2. 2.
    Antoniou G, Groth P, van Harmelen F, Hoekstra R (2012) A semantic web primer, 3rd edn. MIT Press, CambridgeGoogle Scholar
  3. 3.
    Aumueller D, Do HH, Massmann S, Rahm E (2005) Schema and ontology matching with COMA++. In: Proceedings of the 2005 ACM SIGMOD international conference on Management of data, ACM, pp 906–908Google Scholar
  4. 4.
    Barney B (2016) Introduction to parallel computing.
  5. 5.
    Carver RH, Tai KC (2005) Modern multithreading: implementing, testing, and debugging multithreaded Java and C++/Pthreads/Win32 programs. Wiley, New YorkCrossRefGoogle Scholar
  6. 6.
    Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms, 3rd edn. MIT press, CambridgezbMATHGoogle Scholar
  7. 7.
    Erdélyi M, Abonyi J (2006) Node similarity-based graph clustering and visualization. In: 7th International symposium of hungarian researchers on computational intelligenceGoogle Scholar
  8. 8.
    Euzenat J, Shvaiko P (2013) Ontology matching. Springer, BerlinCrossRefzbMATHGoogle Scholar
  9. 9.
    Grama A, Gupta A, Karypis G, Kumar V (2003) Introduction to parallel computing. Pearson, Upper Saddle RiverzbMATHGoogle Scholar
  10. 10.
    Gross A, Hartung M, Kirsten T, Rahm E (2010) On matching large life science ontologies in parallel. In: Lambrix P, Kemp GJL (eds) International Conference on Data Integration in the Life Sciences, Springer, Gothenburg, Sweden, pp 35–49CrossRefGoogle Scholar
  11. 11.
    Gustafson JL (1988) Reevaluating amdahl’s law. Communications of the ACM 31(5):532–533CrossRefGoogle Scholar
  12. 12.
    Hu W, Qu Y, Cheng G (2008) Matching large ontologies: a divide-and-conquer approach. Data Knowl Eng 67(1):140–160CrossRefGoogle Scholar
  13. 13.
    Jaccard P (1901) Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Société Vaud Sci Nat 37:547–579 (in French)Google Scholar
  14. 14.
    Jean-Mary YR, Shironoshita EP, Kabuka MR (2009) Ontology matching with semantic verification. Web Semant Sci Serv Agents World Wide Web 7(3):235–251CrossRefGoogle Scholar
  15. 15.
    Lambrix P, Kaliyaperumal R (2013) A session-based approach for aligning large ontologies. In: Cimiano P, Corcho Ó, Presutti V, Hollink L, Rudolph S (eds) Extended Semantic Web Conference, Springer, Montpellier, France, pp 46–60Google Scholar
  16. 16.
    Lambrix P, Tan H (2006) Sambo—a system for aligning and merging biomedical ontologies. Web Semant Sci Serv Agents World Wide Web 4(3):196–206CrossRefGoogle Scholar
  17. 17.
    Li J, Tang J, Li Y, Luo Q (2009) Rimom: a dynamic multistrategy ontology alignment framework. IEEE Trans Knowl Data Eng 21(8):1218–1232CrossRefGoogle Scholar
  18. 18.
    Madhavan J, Bernstein PA, Domingos P, Halevy AY (2002) Representing and reasoning about mappings between domain models. In: AAAI-02 proceedings, pp 80–86Google Scholar
  19. 19.
    Maedche A (2002) Ontology learning for the semantic web. Springer, BerlinCrossRefzbMATHGoogle Scholar
  20. 20.
    Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39–41CrossRefGoogle Scholar
  21. 21.
    Mittra T, Ali MM (2014) Ontology matching by applying parallelization and distribution of matching task within clustering environment. In: IEEE 2014 international conference on electrical and computer engineering (ICECE), pp 445–448Google Scholar
  22. 22.
    Nagy M, Vargas-Vera M, Motta E (2007) DSSIM: managing uncertainty on the semantic web. In: Proceedings of the 2nd international conference on ontology matching, vol 304, CEUR-WS. org, pp 160–169Google Scholar
  23. 23.
    Otero-Cerdeira L, Rodríguez-Martínez FJ, Gómez-Rodríguez A (2015) Ontology matching: a literature review. Expert Syst Appl 42(2):949–971CrossRefGoogle Scholar
  24. 24.
    Rahm E (2011) Towards large-scale schema and ontology matching. In: Bellahsene Z, Bonifati A, Rahm E (eds) Schema matching and mapping, Springer, Berlin, Heidelberg, pp 3–27CrossRefGoogle Scholar
  25. 25.
    Seddiqui MH, Aono M (2009) An efficient and scalable algorithm for segmented alignment of ontologies of arbitrary size. Web Semant Sci Serv Agents World Wide Web 7(4):344–356CrossRefGoogle Scholar
  26. 26.
    Shvaiko P, Euzenat J (2013) Ontology matching: state of the art and future challenges. IEEE Trans Knowl Data Eng 25(1):158–176CrossRefGoogle Scholar
  27. 27.
    Solomonik E, Carson E, Knight N, Demmel J (2014) Tradeoffs between synchronization, communication, and work in parallel linear algebra computations. Technical Reports. UCB/EECS-2014-8, EECS Department, University of California, Berkeley.
  28. 28.
    Staab S, Studer R (eds) (2004) Handbook on ontologies. International handbooks on information systems. Springer, BerlinGoogle Scholar
  29. 29.
    Tenschert A, Assel M, Cheptsov A, Gallizo G (2009) Parallelization and distribution techniques for ontology matching in urban computing environments. In: Proceedings of the fourth international workshop on ontology matchingGoogle Scholar
  30. 30.
    Valiant LG (1990) A bridging model for parallel computation. Commun ACM 33(8):103–111CrossRefGoogle Scholar
  31. 31.
    Yu L (2011) A developer’s guide to the semantic web. Springer, BerlinCrossRefGoogle Scholar

Copyright information

© CSI Publications 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh

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