Abstract
We present, in this paper, a duplicate detection method in semantically annotated Web data tables, driven by a domain Termino-Ontological Resource (TOR). Our method relies on the fuzzy semantic annotations automatically associated with the Web data tables. A fuzzy semantic annotation is automatically associated with each row of a Web data table. It corresponds to the instantiation of a composed concept of the domain TOR, which represents the semantic n-ary relationship that exists between the columns of the Web data table. A fuzzy semantic annotation contains fuzzy values expressed as fuzzy sets. We propose an automatic duplicate detection method which consists in detecting the pairs of duplicate fuzzy semantic annotations and relies on (i) knowledge declared in the domain TOR and on (ii) similarity measures between fuzzy sets. Two new similarity measures are defined to compare both, the symbolic fuzzy values and the numerical fuzzy values. Our method has been tested on a real application in the domain of chemical risk in food.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hignette, G., Buche, P., Dibie-Barthélemy, J., Haemmerlé, O.: Fuzzy annotation of web data tables driven by a domain ontology. In: Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P., Heath, T., Hyvönen, E., Mizoguchi, R., Oren, E., Sabou, M., Simperl, E. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 638–653. Springer, Heidelberg (2009)
Zadeh, L.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Saïs, F., Pernelle, N., Rousset, M.C.: Combining a logical and a numerical method for data reconciliation. J. Data Semantics 12, 66–94 (2009)
Buche, P., Haemmerlé, O.: Towards a unified querying system of both structured and semi-structured imprecise data using fuzzy view. In: Ganter, B., Mineau, G.W. (eds.) ICCS 2000. LNCS, vol. 1867, pp. 207–220. Springer, Heidelberg (2000)
Buche, P., Dibie-Barthélemy, J., Chebil, H.: Flexible sparql querying of web data tables driven by an ontology. In: Andreasen, T., Yager, R.R., Bulskov, H., Christiansen, H., Larsen, H.L. (eds.) FQAS 2009. LNCS, vol. 5822, pp. 345–357. Springer, Heidelberg (2009)
Roche, C., Calberg-Challot, M., Damas, L., Rouard, P.: Ontoterminology - a new paradigm for terminology. In: KEOD, pp. 321–326 (2009)
Reymonet, A., Thomas, J., Aussenac-Gilles, N.: Modelling ontological and terminological resources in OWL DL. In: OntoLex-Workshop at ISWC 2007 (2007)
Dubois, D., Prade, H.: The three semantics of fuzzy sets. Fuzzy Sets and Systems 90, 141–150 (1997)
Bouchon-Meunier, B., Rifqi, M., Bothorel, S.: Towards general measures of comparison of objects. Fuzzy Sets and Systems 11, 143–153 (1996)
Bilenko, M., Mooney, R.J.: Adaptive duplicate detection using learnable string similarity measures. In: KDD, pp. 39–48 (2003)
Jaccard, P.: Etude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)
Tversky, A.: Features of similarity. Psychological Review 84, 327–352 (1977)
Largeron, C., Kaddour, B., Fernandez, M.: Softjaccard: une mesure de similarité entre ensembles de chaînes de caractères pour l’unification d’entités nommées. In: Extaction et Gestion des Connaissances (EGC) (2009)
Hsieh, C.H., Chen, S.H.: Similarity of generalized fuzzy numbers with graded mean integration represntation. In: Proc. 8th IFSA World Congr., vol. 2, pp. 551–555 (1999)
Chen, S.M.: New methods for subjective mental workload assessment and fuzzy risk analysis. Cybernetics and Systems 27, 449–472 (1996)
Chen, S.J., Chen, S.M.: Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. IEEE 11(1), 45–56 (2003)
Cohn, D.A., Atlas, L.E., Ladner, R.E.: Improving generalization with active learning. Machine Learning 15(2), 201–221 (1994)
Tejada, S., Knoblock, C.A., Minton, S.: Learning object identification rules for information integration. Inf. Syst. 26(8), 607–633 (2001)
Saïs, F., Pernelle, N., Rousset, M.C.: L2R: A logical method for reference reconciliation. In: AAAI Conference on Artificial Intelligence, pp. 329–334 (2007)
Gonzalez, H., Halevy, A.Y., Jensen, C.S., Langen, A., Madhavan, J., Shapley, R., Shen, W.: Google fusion tables: data management, integration and collaboration in the cloud. In: SoCC, pp. 175–180 (2010)
Gonzalez, H., Halevy, A.Y., Jensen, C.S., Langen, A., Madhavan, J., Shapley, R., Shen, W., Goldberg-Kidon, J.: Google fusion tables: web-centered data management and collaboration. In: SIGMOD Conference, pp. 1061–1066 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Buche, P., Dibie-Barthélemy, J., Khefifi, R., Saïs, F. (2011). An Ontology-Based Method for Duplicate Detection in Web Data Tables. In: Hameurlain, A., Liddle, S.W., Schewe, KD., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2011. Lecture Notes in Computer Science, vol 6860. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23088-2_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-23088-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23087-5
Online ISBN: 978-3-642-23088-2
eBook Packages: Computer ScienceComputer Science (R0)