Introducing Artificial Neural Network in Ontologies Alignement Process

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 185)

Abstract

During automated/semi-automated alignment across myriad ontologies, different similarity measures of different categories such as string, linguistic, and structural based similarity measures, contribute each to some extend to alignment results. A weights vector must, therefore, be assigned to these similarity measures, if a more accurate and meaningful alignment result is favored. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics and/or prior domain knowledge. In this paper, we take an artificial neural network approach to learn and adjust these weights, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. XMap++ is applied to benchmark tests at OAEI campaign 2010. Results show that neural network boosts the performance in most cases, and that the proposed novel approach is competitive with top-ranked system.

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References

  1. 1.
    Gruber, T.: A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition 5(2), 199–220 (1993)CrossRefGoogle Scholar
  2. 2.
    Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)MATHGoogle Scholar
  3. 3.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)CrossRefGoogle Scholar
  4. 4.
    Tumer, K., Ghosh, J.: Classifier Combining: Analytical Results and Implications. In: 13th National Conference on Artificial Intelligence, Working Notes from the Workshop, Integrating Multiple Learned Models, Protland, Oregon (1996)Google Scholar
  5. 5.
    Chortaras, A., Stamou, G., Stafylopatis, A.: Learning Ontology Alignments Using Recursive Neural Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005, Part II. LNCS, vol. 3697, pp. 811–816. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Mao, M., Peng, Y., Spring, M.: An Adaptive Ontology Mapping Approach with Neural Network based Constraint Satisfaction. Journal of Web Semantics 8(1), 14–25 (2010)CrossRefGoogle Scholar
  7. 7.
    Euzenat, J., Ferrara, A., Meilicke, C., Pane, J., Scharffe, F., Shvaiko, P., Stuckenschmidt, H., Šváb-Zamazal, O., Svátek, V., Trojahn, C.: Results of the Ontology Alignment Evaluation Initiative 2010. In: Proceedings of the Fifth International Workshop on Ontology Matching, OM 2010. CEUR-WS, vol. 689 (2010)Google Scholar
  8. 8.
    Bellahsene, Z., Duchateau, F.: Tuning for schema matching. In: Bellahsene, Z., Bonifati, A., Rahm, E. (eds.) Schema Matching and Mapping. Springer Data-Centric Systems and Applications Series (2011)Google Scholar
  9. 9.
    Duchateau, F., Coletta, R., Bellahsene, Z., Miller, R.J.: (Not) yet another matcher. In: Proc. CIKM, poster paper (2009)Google Scholar
  10. 10.
    Li, Y., Li, J.Z., Zhang, D., Tang, J.: Result of Ontology Alignment with RiMOM at OAEI’06. Ontology Matching (2006)Google Scholar
  11. 11.
    Doan, A., Madhaven, J., et al.: Learning to match ontologies on the semantic web. VLDB Journal 12(4), 303–319 (2003)CrossRefGoogle Scholar
  12. 12.
    Djeddi, W., Khadir, M.T.: A Dynamic Multistrategy Ontology Alignment Framework Based on Semantic Relationships using WordNet. In: Proceedings of the 3rd International Conference on Computer Science and its Applications, CIIA 2011, Saida, Algeria, December 13-15, pp. 149–154 (2011)Google Scholar
  13. 13.
    Fellbaum, C.: WordNet: An electronic lexical database. MIT Press, Cambridge (1998)MATHGoogle Scholar
  14. 14.
    Jiamjitvanich, K., Yatskevich, M.: Reducing polysemy in WordNet. In: Proceedings of the 4th International Workshop on Ontology Matching, OM 2009, Washington DC, USA, pp. 260–261 (2009)Google Scholar
  15. 15.
    Dey, A., Salber, D., Abowd, G.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human-Computer Interaction 16, 97–166 (2001)CrossRefGoogle Scholar
  16. 16.
    Dourish, P.: Seeking a foundation for context-aware computing. Human-Computer Interaction 16(2-3) (2001)Google Scholar
  17. 17.
    Chalmers, M.: A Historical View of Context. Computer Supported Cooperative Work 13(3), 223–247 (2004)CrossRefGoogle Scholar
  18. 18.
    Reidmiller, M., et al.: A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP algorithm. In: IEEE Inter. Conf. on Neural Network, pp. 586–591 (1993)Google Scholar
  19. 19.
    Heaton, J.: Programming Neural Networks with Encog3 in Java, 2nd edn. (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.LabGED, Computer Science DepartmentUniversity Badji MokhtarAnnabaAlgeria

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