A Semi-automatic Mapping Selection in the Ontology Alignment Process

Hafed Nefzi, Mohamed Farah, Imed Riadh Farah, Basel Solaiman

Abstract

Ontologies are considered as one of the most powerful tools for knowledge representation and reasoning. Thus, they are considered as a fundamental support for image annotation, indexing and retrieval. In order to build a remote sensing satellite image ontology that models the geographic objects that we find in a scene, their characteristics as well as their relationships, we propose to reuse existing geographic ontologies to enrich an ontological core. Reusing high quality resources (called source ontologies) helps ensuring a good quality for the extracted knowledge, and alleviating the conceptualization stage, i.e. avoiding building a new ontology from scratch. Ontology alignment is an important phase within the enrichment process. It is a process that allows discovering mappings between core and source ontologies, where each mapping is a couple of entities brought from each ontology and linked together either by an equivalence or a subsumption relationship. Such relationships are based on various similarity measures. In this paper, we first present a brief literature review of existing theoretical frameworks for similarity measures, then we describe a new alignment approach based on a semi-automatic mapping selection process that needs little human intervention. First experiments show the benefit from using the proposed approach.

References

  1. Charlet, j., Szulman, S., Aussenac-Gilles, N., Nazarenko, Hernandez, N., Nadah, N., Sardet, E., Delahousse, J., Durand, N., Derivaux, S., Forestier, G., Wemmert, C., Gancarski, P., Boussaid, O., and Puissant, A. (2007). Ontology-based object recognition for remote sensing image interpretation. In IEEE International Conference on Tools with Artificial Intelligence, pages 472- 479, Greece.
  2. Egenhofer, M. (2002). Toward the semantic geospatial web. In 10th ACM International Symposium on Advances in Geographic Information Systems, 10.1145/585147.585148, pages 1-4. ACM.
  3. Gardenfors, P. (2000). Conceptual Spaces: The Geometry of Thought. Massachusetts Institute of technology, Cambridge, 2004 edition.
  4. Gentner, D. (1989). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7:155- 170.
  5. Gesbert, N. (2005). Etude de la formalisation des spcifications de bases de donnes gographiques en vue de leur intgration. PhD thesis, Universit de Marne la Valle et IGN.
  6. Goldstone, R. L. (1994). Similarity, interactive activation, and mapping. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20:3-28.
  7. Goldstone, R. L. (1999). Similarity. In The MIT encyclopedia of the cognitive sciences, pages 763-765.
  8. Goldstone, R. L. (2005). Similarity. In Cambridge handbook of thinking and reasonning, pages 13-36.
  9. Hahn, U., Close, J., and Graf, M. (2009). Transformation direction influences shape similarity judgements. Psychological science, pages 447-454.
  10. Hamming, R. W. (1950). Error detecting and error correcting codes. Bell System Technical Journal, pages 147- 160.
  11. Holyoak, K. J. and Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science, 13:295- 355.
  12. Hudelot, C., Atif, J., and Bloch, I. (2006). Ontologie de relations spatiales floues pour l'interprtation d'images. In Rencontres francophones sur la Logique Floue et ses Applications, Toulouse, France. LFA 2006.
  13. Imai, S. (1977). Pattern similarity and cognitive transformations. Acta Psychologica, 41(6):433-447.
  14. Jacquet-Lagrèze, E., Meziani, R., and Slowinski, R. (1987). Molp with an interactive assessment of a piecewise utility function. Eur. J. Oper. Res, 31(3):350-357.
  15. Jacquet-Lagrèze, E. and Siskos, Y. (1982). Assessing a set of additive utility functions for multicriteria decision making: the UTA method. European Journal of Operational Research, 10:151-164.
  16. Jiang, J.and Conrath, D. (1997). Semantic similarity based on corpus statistics and lexical taxonomy. In International Conference on Research in Computational Linguistics, Taiwan.
  17. Keeney, R. and Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. J. Wiley, New York.
  18. Leacock, C.and Chodorow, M. (1998). Combining local context and wordnet similarity for word sense identification. In MIT Press, pages 265-283.
  19. Lin, D. (1998). An information-theoretic definition of similarity. In Madison, M.-K., editor, the fifteenth International Conference on Machine Learning, pages 296- 304.
  20. Markman, A. B., . G. D. (1993). Structural alignment during similarity comparisons. Cognitive Psychology, 25:431-467.
  21. Nefzi, H., Messaoudi, W., Farah, M., and Farah, I. R. (2013). Vers une ontologie riche ddie l'imagerie satellitaire par rutilisation de ressources existantes. In TAIMA'2013, pages 35-46.
  22. Parent, C., Spaccapietra, S., and Zimanyi, E. (1998). modle conceptuel spatio-temporel. Revue internationale de gomantique, (7):317-352.
  23. Resnik, P. (1995). Using information content to evaluate semantic similarity in taxonomy. In 14th International Joint Conference on Artificial Intelligence, Montreal.
  24. Resnik, P. (1999). Semantic similarity in a taxonomy: An information based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research.
  25. Roy, B. (1991). The outranking approach and the foundations of ELECTRE methods. Theory and Decision, 31:49-73.
  26. Schlicker, A., Domingues, F. S., Rahnenf, J., and Lengauer, T. (2006). A new measure for functional similarity of gene products based on gene ontology. BMC Bioinformatics, 7(302).
  27. Schwering, A. and Raubal, M. (2005). Measuring semantic similarity between geospatial conceptual regions. In the First International Conference on GeoSpatial Semantics, Mexico City,Mexico.
  28. Shvaiko, P. and Euzenat, J. (2008). Ten challenges for ontology matching. In in Computer Science, L. N., editor, OTM'08: Proceedings of the OTM 2008 Confederated International, volume 5332, pages 1164-1182. OTM, Springer.
  29. Sjoberg, L. (1972). A cognitive theory of similarity. Goteborg Psychological Reports, 2.
  30. Tversky, A. (1977). Features of similarity. Psychological Review, 84(4):327-352.
  31. Wiener Ehrlish, W., Bart, W., and Millward, R. (1980). An analysis of generative representation systems. mathematical psychology, pages 219-246.
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Paper Citation


in Harvard Style

Nefzi H., Farah M., Riadh Farah I. and Solaiman B. (2014). A Semi-automatic Mapping Selection in the Ontology Alignment Process . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014) ISBN 978-989-758-049-9, pages 459-466. DOI: 10.5220/0005162204590466


in Bibtex Style

@conference{keod14,
author={Hafed Nefzi and Mohamed Farah and Imed Riadh Farah and Basel Solaiman},
title={A Semi-automatic Mapping Selection in the Ontology Alignment Process},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014)},
year={2014},
pages={459-466},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005162204590466},
isbn={978-989-758-049-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014)
TI - A Semi-automatic Mapping Selection in the Ontology Alignment Process
SN - 978-989-758-049-9
AU - Nefzi H.
AU - Farah M.
AU - Riadh Farah I.
AU - Solaiman B.
PY - 2014
SP - 459
EP - 466
DO - 10.5220/0005162204590466