Introducing artificial neural network in ontologies alignment process

• Autorzy: Djeddi W. E. , Khadir M. T.
• Języki publikacji: EN

Abstrakty

EN

Ontology alignment uses different similaritymeasures of different categories such as string, linguistic, and structural based similarity measures to understand ontologies’ semantics. A weights vector must, therefore, be assigned to these similarity measures, if a more accurate and meaningful alignment result is favored. Combining multiple measures into a single similarity metric has been traditionally solved using weights determined manually by an expert, Or calculated through general methods (e.g. average or sigmoid function) that do not provide optimal results. In this paper, we propose an artificial neural network algorithm to ascertain how to Combie multiple similarity measures into a single aggregated metric with the final aim of improving the ontology alignment quality. 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.

Słowa kluczowe

EN

artificial neural network; training; ontology alignment; WordNet; XMap++

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2012
• Tom: Vol. 41, no. 4
• Strony: 743--759
• Opis fizyczny: Bibliogr. 31 poz., il., wykr.

Twórcy

autor

Djeddi W. E.

• LabGED, Computer Science Department, University Badji Mokhtar, PO-Box 12, 23000 Annaba, Algeria

autor

Khadir M. T.

• LabGED, Computer Science Department, University Badji Mokhtar, PO-Box 12, 23000 Annaba, Algeria

Bibliografia

• 1. Bernstein, P.A. and Melnik, S. (2007) Model management 2.0: Manipulating richer mappings. In: SIGNMOD’07. Proc. of the 2007 ACM SIG-MOD International Conference on Management of Data. ACM Press, New York, 1-12.
• 2. Bellahsene, Z., Bonifati, A. and Rahm, E., eds. (2011) Schema Matching and Mapping. Springer ++Data–Centric Systems and Applications Series.
• 3. Chalmers, M. (2004) A Historical View of Context. Computer supported cooperative work 13(3), 223–247.
• 4. Chortaras, A., Stamouet, G. B., and Stafylopatis, A. (2005) Learning Ontology Alignments Using Recursive Neural Networks. In: Proceedings of the 15th international conference on ANN: formal models and their applications, Part II. Springer, 811–816.
• 5. Dey, A., D., Salber and Abowd, G. (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. J. Human-Computer Interaction (HCI) 16(2-4), 97–166.
• 6. Djeddi, W. and Khadir, M. T. (2011) A Dynamic Multistrategy Ontology Alignment Framework Based on Semantic Relationships usingWordNet.In: Proceedings of the 3rd International Conference on Computer Science and its Applications (CIIA’11), December 13-15, Saida, Algeria. CEUR–WS.org, 149–154.
• 7. Djeddi, W. and Khadir, M. T. (2012) Introducing Artificial Neural Network in Ontologies Alignement Process. New Trends in Databases and Information Systems Advances in Intelligent Systems and Computing 185, 175–186.
• 8. Doan, A.,Madhaven, J., Dhamankar R., Domingos P. and Halevy, A.Y. (2003) Learning to match ontologies on the semantic web. The International Journal on Very Large Data Bases 12 (4), 303–319.
• 9. Dourish, P. (2001) Seeking a foundation for context-aware computing. J. Human-Computer Interaction (HCI) 16(2-3).
• 10. Duchateau, F., Coletta, R., Bellahsene, Z. and Miller, R. J. (2009) (Not) yet another matcher. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009. ACM Press, 1537–1540.
• 11. Euzenat, J., Bach, T. et al. (2004) State of the art on ontology alignment. Knowledge web NoE. Technical Report, deliverable 2.2.3. Statistical Research Division, Bureau of the Census, Washington.
• 12. Euzenat, J., Ferrara, A., Meilicke et al. (2010) Results of the Ontology Alignment Evaluation Initiative 2010. In: Proceedings of the Fifth International Workshop on Ontology Matching (OM-2010). CEUR-WS 689.
• 13. Euzenat, J. and Shvaiko, P. (2007) Ontology Matching. Springer, Berlin, Heidelberg, New York.
• 14. Falconer, S.M. and Storey, M. (2007) Cognitive support for human-guided mapping systems. Technical Report DCS-318-IR, University of Victoria, Victoria, BC, Canada.
• 15. Falconer, S.M., Noy, N.F. and Storey, M. (2006) Towards understanding the needs of cognitive support for ontology mapping. In: International Workshop on Ontology Matching, ISWC 2006. CEUR-WS 225.
• 16. Fellbaum, C. (1998) WordNet: An Electronic Lexical Database. MIT Press, Cambridge MA.
• 17. Gracia, J., Bernad, J. and Mena, E. (2011) OntologyMatching with CIDER: Evaluation Report for OAEI 2011. In: Proceedings of 6th Ontology Matching Workshop (OM’11), at 10th International Semantic Web Conference (ISWC’11), Bonn (Germany). CEUR-WS 814.
• 18. Gruber, T. (1993) A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition 5(2), 199–220.
• 19. Heaton, J. (2011) Programming Neural Networks with Encog3 in Java. 2nd ed. Heaton Research, Chesterfield.
• 20. Kittler, J., Hatef, M., Duin, R.P.W. and Matas, J. (1998) On Combining Classifiers. IEEE Trans. On Pattern Analysis and Machine Intelligence, 20, 226–239.
• 21. Jiamjitvanich, K. and Yatskevich, M. (2009) Reducing polysemy inWordNet. In: Proceedings of the 4th International Workshop on Ontology Matching(OM-2009). CEUR–WS.org, 260–261.
• 22. Kalfoglou, Y. and Schorlemmer, M. (2003) Ontologymapping: the state of the art. Knowledge Engineering Review 18(1), 1–31.
• 23. Li, Y., Li, J.Z., Zhang, D. and Tang, J. (2006) Result of Ontology Alignment with RiMOM at OAEI’06. Ontology Matching. CEUR Workshop proceedings, 225. CEUR–WS.org
• 24. Mao, M., Peng, Y. and Spring, M. (2010) An Adaptive OntologyMapping Approach with Neural Network based Constraint Satisfaction. Journal of Web Semantics 8(1), 14–25.
• 25. McLachlan, G. J., Do, K. A. and Ambroise, C. (2004) Analyzing microarray gene expression data. In: Wiley Series in Probability and Statistics. Wiley-Interscience, New Jersey.
• 26. Melnik, S., Garcia-Molina, H. and Rahm, E. (2001) Similarity flooding: A versatile graph matching algorithm and its application to schema matching. Proceedings of ICDE, IEEE Computer Society, Washington, DC, 117–128.
• 27. Noy, N. and Musen, M.A. (2000) PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment. In: Proceedings of the 7th National Conference on Artificial Intelligence. AAAI Press, 450–455.
• 28. Noy, N. and Musen, M.A. (2001) Anchor PROMPT: Using Non-Local Context for Semantic Mapping. In: B. Nebel, ed., Proc. of the 17th International Joint Conference on AI, IJCAI 2001. Morgan Kauffmann, Seattle, Washington, 63–70.
• 29. Reidmiller, M. et al. (1993) A Direct Adaptive Method for Faster Back-propagation Learning: The RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN). IEEE Press, 586–591.
• 30. Shraiko, P. and Euzenat, J. (2013) Ontology Matching: State of the art. and future challenges. IEEE Trans.on Knowledge and Data Engineering, 25 (1), 158–176.
• 31. Tumer, K. and Ghosh, J. (1996) Classifier Combining: Analytical Results and Implications. In: Proceedings of the 13th National Conference on Artificial Intelligence. AAAI Press.