Skip to main content
Springer Nature Link
Log in

MAPSOM: User Involvement in Ontology Matching

  • Conference paper
  • First Online:
Semantic Technology (JIST 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8388))

Included in the following conference series:

  • 1197 Accesses

Abstract

This paper presents a semi-automatic similarity aggregating system for ontology matching problem. The system consists of two main parts. The first part is aggregation of similarity measures with the help of self-organizing map. The second part incorporates user feedback for refining self-organizing map outcomes. The system calculates different similarity measures (e.g., string-based similarity measure, WordNet-based similarity measure...) to cover different causes of semantic heterogeneity. The next step is similarity aggregation by means of the self-organizing map and the ward clustering. The final step is the active learning phase for results tuning. We implemented this idea as MAPSOM framework. Our experimental results show that MAPSOM framework can be used for problems where the highest precision is needed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://oaei.ontologymatching.org/2013/benchmarks/

References

  1. Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35(12), 29–38 (1992)

    Article  Google Scholar 

  2. Wache, H., Voegele, T., Visser, U., Stuckenschmidt, H., Schuster, G., Neumann, H., Hbner, S.: Ontology-based integration of information-a survey of existing approaches. In: Proceedings of IJCAI Workshop on Ontologies and Information Sharing, pp. 108–117 (2001)

    Google Scholar 

  3. Kashyap, V., Sheth, A.: Semantic and schematic similarities between database objects: a context-based approach. Int. J. Very Large Data Bases 5(4), 276–304 (1996)

    Article  Google Scholar 

  4. Kim, W., Seo, J.: Classifying schematic and data heterogeneity in multidatabase systems. Computer 24(12), 12–18 (1991)

    Article  Google Scholar 

  5. Goh, C.H.: Representing and reasoning about semantic conflicts in heterogeneous information systems. Ph.D. thesis (1996)

    Google Scholar 

  6. Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum.-Comput. Stud. 43(5), 907–928 (1995)

    Article  Google Scholar 

  7. Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  8. Ichise, R.: Machine learning approach for ontology mapping using multiple concept similarity measures. In: Proceedings of the 7th IEEE/ACIS International Conference on Computer and Information Science, pp. 340–346 (2008)

    Google Scholar 

  9. Jirkovský, V., Obitko, M.: Ontology mapping approach for fault classification in multi-agent systems. In: Proceedings of the IFAC Conference on Manufacturing Modelling, Management, and Control, pp. 951–956 (2013)

    Google Scholar 

  10. Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  11. Valtchev, P., Euzenat, J.: Dissimilarity measure for collections of objects and values. In: Liu, X., Cohen, P., Berthold, M. (eds.) IDA 1997. LNCS, vol. 1280, pp. 259–272. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  12. Melnik, S., Rahm, E., Bernstein, P.A.: Rondo: a programming platform for generic model management. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 193–204. ACM (2003)

    Google Scholar 

  13. Aumueller, D., Do, H.H., Massmann, S., Rahm, E.: Schema and ontology matching with coma. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 906–908. ACM (2005)

    Google Scholar 

  14. Jian, N., Hu, W., Cheng, G., Qu, Y.: Falcon-ao: Aligning ontologies with falcon. In: Proceedings of K-CAP Workshop on Integrating Ontologies, pp. 85–91 (2005)

    Google Scholar 

  15. Bernstein, P.A., Melnik, S., Churchill, J.E.: Incremental schema matching. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 1167–1170 (2006)

    Google Scholar 

  16. Giunchiglia, F., Yatskevich, M., Avesani, P., Shvaiko, P.: A large dataset for the evaluation of ontology matching. Knowl. Eng. Rev. 24(2), 137–157 (2009)

    Article  Google Scholar 

  17. Shvaiko, P., Euzenat, J.: Ten challenges for ontology matching. In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part II. LNCS, vol. 5332, pp. 1164–1182. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  18. Do, H.H., Rahm, E.: Matching large schemas: approaches and evaluation. Inf. Syst. 32(6), 857–885 (2007)

    Article  Google Scholar 

  19. Falconer, S.M., Storey, M.-A.D.: A cognitive support framework for ontology mapping. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 114–127. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  20. Mocan, A., Cimpian, E.: An ontology-based data mediation framework for semantic environments. Int. J. Seman. Web Inf. Syst. 3(2), 69–98 (2007)

    Article  Google Scholar 

  21. Robertson, G.G., Czerwinski, M.P., Churchill, J.E.: Visualization of mappings between schemas. In: Proceedings of the SIGCHI Conference on Human Factors in Computing System, pp. 431–439. ACM (2005)

    Google Scholar 

  22. Zhao, L., Ichise, R.: Aggregation of similarity measures in ontology matching. In: Proceedings of the 5th International Workshop on Ontology Matching, pp. 232–233 (2010)

    Google Scholar 

  23. Curino, C., Orsi, G., Tanca, L.: X-som: A flexible ontology mapper. In: Proceedings of the 18th International Workshop on Database and Expert Systems Applications, pp. 424–428. IEEE (2007)

    Google Scholar 

  24. Tran, Q.V., Ichise, R., Ho, B.Q.: Clusterbased similarity aggregation for ontology matching. In: Proceedings of the 6th International Workshop on Ontology Matching, pp. 142–147 (2011)

    Google Scholar 

  25. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)

    Article  Google Scholar 

  26. Kaski, S., Kohonen, T.: Exploratory data analysis by the self-organizing map: Structures of welfare and poverty in the world. In: Proceedings of the 3rd International Conference on Neural Networks in the Capital Markets (1996)

    Google Scholar 

  27. Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison (2009)

    Google Scholar 

  28. Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Croft, B.W., van Rijsbergen, C.J. (eds.) Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3–12. Springer, New York (1994)

    Google Scholar 

  29. Lewis, D.D., Catlett, J.: Heterogenous uncertainty sampling for supervised learning. In: Proceedings of the 11th International Conference on Machine Learning, pp. 148–156 (1994)

    Google Scholar 

  30. Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)

    Article  Google Scholar 

Download references

Acknowledgements

This research has been supported by the Grant Agency of the Czech Technical University in Prague, grant No. SGS12/188/OHK3/3T/13.

Author information

Authors and Affiliations

  1. Czech Technical University in Prague, Zikova 1903/4, Prague, Czech Republic

    Václav Jirkovský

  2. Rockwell Automation Research and Development Center, Pekařská 695/10a, Prague, Czech Republic

    Václav Jirkovský

  3. National Institute of Informatics, Tokyo, 101-8430, Japan

    Ryutaro Ichise

Authors
  1. Václav Jirkovský
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryutaro Ichise
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Václav Jirkovský .

Editor information

Editors and Affiliations

  1. Yonsei University, Seoul, Korea, Republic of (South Korea)

    Wooju Kim

  2. Indiana University, Bloomington, Indiana, USA

    Ying Ding

  3. Seoul National University, Seoul, Korea, Republic of (South Korea)

    Hong-Gee Kim

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Jirkovský, V., Ichise, R. (2014). MAPSOM: User Involvement in Ontology Matching. In: Kim, W., Ding, Y., Kim, HG. (eds) Semantic Technology. JIST 2013. Lecture Notes in Computer Science(), vol 8388. Springer, Cham. https://doi.org/10.1007/978-3-319-06826-8_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06826-8_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06825-1

  • Online ISBN: 978-3-319-06826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics