Abstract
In this paper, we investigate a principled approach for defining and discovering probabilistic mappings between two taxonomies. First, we compare two ways of modeling probabilistic mappings which are compatible with the logical constraints declared in each taxonomy. Then we describe a generate and test algorithm which minimizes the number of calls to the probability estimator for determining those mappings whose probability exceeds a certain threshold. Finally, we provide an experimental analysis of this approach.
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References
Adams, E.: A Primer of Probability logic, CSLI. Stanford University, Stanford (1998)
Adjiman, P., Chatalic, P., Goasdoué, F., Rousset, M.C., Simon, L.: Distributed reasoning in a peer-to-peer setting: Application to the semantic web. Journal of Artificial Intelligence Research (JAIR) 25, 269–314 (2006)
Aumueller, D., Do, H.H., Massmann, S., Rahm, E.: Schema and ontology matching with COMA++. In: SIGMOD 2005: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 906–908. ACM, New York (2005)
Beeri, C., Dowd, M., Fagin, R., Statman, R.: On the structure of Armstrong relations for functional dependencies. Journal of the ACM (JACM) 31(1), 30–46 (1984)
Benjelloun, O., Sarma, A.D., Halevy, A.Y., Widom, J.: ULDBs: Databases with uncertainty and lineage. In: VLDB, pp. 953–964 (2006)
Castano, S., Ferrara, A., Lorusso, D., Näth, T.H., Möller, R.: Mapping validation by probabilistic reasoning. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 170–184. Springer, Heidelberg (2008)
Castano, S., Ferrara, A., Messa, G.: Results of the H-MATCH ontology matchmaker in OAEI 2006. In: Proceedings of the ISWC 2006 Workshop on Ontology Matching, Athens, GA, USA (2006)
Castano, S., Ferrara, A., Montanelli, S.: H-MATCH: an algorithm for dynamically matching ontologies in peer-based systems. In: SWDB, pp. 231–250 (2003)
Chiticariu, L., Hernández, M.A., Kolaitis, P.G., Popa, L.: Semi-automatic schema integration in clio. In: VLDB, pp. 1326–1329 (2007)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. The MIT Press, Cambridge (September 2001)
Dalvi, N.N., Suciu, D.: Answering queries from statistics and probabilistic views. In: VLDB, pp. 805–816 (2005)
David, J., Guillet, F., Gras, R., Briand, H.: An interactive, asymmetric and extensional method for matching conceptual hierarchies. In: EMOI-INTEROP Workshop, Luxembourg (2006)
Dean, M., Schreiber, G.: OWL web ontology language reference. W3C recommendation, W3C (February 2004)
Degroot, M.H.: Optimal Statistical Decisions (Wiley Classics Library). Wiley-Interscience, Hoboken (April 2004)
Do, H.H., Rahm, E.: COMA - a system for flexible combination of schema matching approaches. In: VLDB (2002)
Doan, A., Domingos, P., Levy, A.Y.: Learning mappings between data schemas. In: Proceedings of the AAAI 2000 Workshop on Learning Statistical Models from Relational DatA (2000)
Doan, A., Madhavan, J., Domingos, P., Halevy, A.Y.: Learning to map between ontologies on the Semantic Web. In: WWW, pp. 662–673 (2002)
Dong, X.L., Halevy, A.Y., Yu, C.: Data integration with uncertainty. In: VLDB, pp. 687–698 (2007)
Duchon, P., Flajolet, P., Louchard, G., Schaeffer, G.: Boltzmann samplers for the random generation of combinatorial structures. Comb. Probab. Comput. 13(4-5), 577–625 (2004)
Euzenat, J., Ferrara, A., Hollink, L., Isaac, A., Joslyn, C., Malais, V., Meilicke, C., Nikolov, A., Pane, J., Sabou, M., et al.: Results of the ontology alignment evaluation initiative 2009. In: Fourth International Workshop on Ontology Matching, Washington, DC (2009)
Euzenat, J.: Semantic Precision and Recall for Ontology Alignment Evaluation. In: IJCAI, pp. 348–353 (2007)
Euzenat, J.: Ontology alignment evaluation initiative (July 2008), http://www.oaei.ontologymatching.org/
Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (2007)
Euzenat, J., Valtchev, P.: Similarity-based ontology alignment in OWL-Lite. In: ECAI, pp. 333–337 (2004)
Fagin, R.: Horn clauses and database dependencies. J. ACM 29(4), 952–985 (1982)
Fellbaum, C.: WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press, Cambridge (May 1998)
Flake, G.W., Lawrence, S.: Efficient SVM regression training with SMO. Mach. Learn. 46(1-3), 271–290 (2002)
Gal, A.: Managing uncertainty in schema matching with top-k schema mappings. Journal on Data Semantics 6 (2006)
Gal, A., Anaby-Tavor, A., Trombetta, A., Montesi, D.: A framework for modeling and evaluating automatic semantic reconciliation. The VLDB Journal 14(1), 50–67 (2005), http://www.portal.acm.org.gate6.inist.fr/citation.cfm?id=1053477
Giunchiglia, F., Shvaiko, P., Yatskevich, M.: S-match: an algorithm and an implementation of semantic matching. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 61–75. Springer, Heidelberg (2004)
Hamdi, F., Safar, B., Reynaud, C., Zargayouna, H.: Alignment-based Partitioning of Large-scale Ontologies. In: Guillet, F., Ritschard, G., Zighed, D.A., Briand, H. (eds.) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol. 292, pp. 251–269. Springer, Heidelberg (2010), http://www.hal.inria.fr/inria-00432606/en/
Hamdi, F., Zargayouna, H., Safar, B., Reynaud, C.: TaxoMap in the OAEI 2008 alignment contest. In: Ontology Alignment Evaluation Initiative (OAEI) 2008, Campaign - Int. Workshop on Ontology Matching (2008)
Hayes, P. (ed.) RDF Semantics. W3C Recommendation, World Wide Web Consortium (February 2004), http://www.w3.org/TR/rdf-mt/
Ichise, R., Takeda, H., Honiden, S.: Integrating multiple internet directories by instance-based learning. In: International Joint Conference on Artificial Intelligence (IJCAI), vol. 18, pp. 22–30 (2003)
Ichise, R., Hamasaki, M., Takeda, H.: Discovering relationships among catalogs. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 371–379. Springer, Heidelberg (2004), http://www.citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.5336
Isaac, A., van der Meij, L., Schlobach, S., Wang, S.: An empirical study of instance-based ontology matching. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 253–266. Springer, Heidelberg (2007)
Koller, D., Levy, A., Pfeffer, A.: P-CLASSIC: a tractable probablistic description logic. In: Proceedings of the National Conference on Artificial Intelligence, pp. 390–397 (1997)
Li, W.S., Clifton, C.: SEMINT: a tool for identifying attribute correspondences in heterogeneous databases using neural networks. Data Knowl. Eng. 33(1), 49–84 (2000)
Lin, F., Sandkuhl, K.: A survey of exploiting wordnet in ontology matching. In: Artificial Intelligence in Theory and Practice II, pp. 341–350 (2008)
Madhavan, J., Bernstein, P.A., Doan, A., Halevy, A.: Corpus-based schema matching. In: International Conference on Data Engineering, pp. 57–68 (2005)
Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with cupid. The VLDB Journal, 49–58 (2001), http://www.citeseer.ist.psu.edu/madhavan01generic.html
Mao, M., Peng, Y.: PRIOR system: Results for OAEI 2006. In: Proceedings of the Ontology Alignment Evaluation Initiative, pp. 165–172 (2006)
Melnik, S., Garcia-Molina, H., Rahm, E., et al.: Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: Proceedings of the International Conference on Data Engineering, pp. 117–128 (2002)
Mitchell, T.: Machine Learning. McGraw-Hill Education (ISE Editions) (1997), http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0071154671
Nottelmann, H., Straccia, U.: Information retrieval and machine learning for probabilistic schema matching. Information Processing and Management 43(3), 552–576 (2007)
Nottelmann, H., Straccia, U.: A probabilistic, logic-based framework for automated web director alignment. In: Ma, Z. (ed.) Soft Computing in Ontologies and the Semantic Web. Studies in Fuzziness and Soft Computing, vol. 204, pp. 47–77. Springer, Heidelberg (2006)
Quinlan, R.J.: C4.5: Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann, San Francisco (January 1993), http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/1558602380
Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB Journal 10(4), 334–350 (2001)
Ramesh, G., Maniatty, W., Zaki, M.J.: Feasible itemset distributions in data mining: theory and application. In: PODS, pp. 284–295 (2003)
Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11(95), 130 (1999)
Saïs, F., Pernelle, N., Rousset, M.C.: Combining a logical and a numerical method for data reconciliation. In: Spaccapietra, S. (ed.) Journal on Data Semantics XII. LNCS, vol. 5480, pp. 66–94. Springer, Heidelberg (2009)
Serafini, L., Bouquet, P., Magnini, B., Zanobini, S.: An algorithm for matching contextualized schemas via SAT. In: Proceedings of CONTEXT 2003 (2003)
Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 146–171. Springer, Heidelberg (2005)
Shvaiko, P., Euzenat, J.: Ten challenges for ontology matching. In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part II, pp. 1164–1182. Springer, Heidelberg (2008)
Stumme, G., Maedche, A.: FCA-MERGE: Bottom-Up Merging of Ontologies. In: Proc. of the 17th International Joint Conference on Artificial Intelligence, pp. 225–234 (2001)
Tournaire, R., Petit, J.M., Rousset, M.C., Termier, A.: Discovery of Probabilistic Mappings between Taxonomies: Principles and Experiments (technical report) (2009), http://www.membres-liglab.imag.fr/tournaire/longpaper.pdf
Tournaire, R., Rousset, M.C.: Découverte automatique de correspondances entre taxonomies - internal report (in french) (2008), http://www.membres-liglab.imag.fr/tournaire/irap08.pdf
Van Rijsbergen, C.J.: Information retrieval. Butterworths, London (1975)
Wang, P., Xu, B.: Lily: Ontology alignment results for OAEI 2009. Shvaiko, et al [SEG+ 09] (2009)
Wang, S., Englebienne, G., Schlobach, S.: Learning concept mappings from instance similarity. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 339–355. Springer, Heidelberg (2008), http://www.portal.acm.org.gate6.inist.fr/citation.cfm?id=1483184
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
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Tournaire, R., Petit, JM., Rousset, MC., Termier, A. (2011). Discovery of Probabilistic Mappings between Taxonomies: Principles and Experiments. In: Spaccapietra, S. (eds) Journal on Data Semantics XV. Lecture Notes in Computer Science, vol 6720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22630-4_3
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