Generalized Mongue-Elkan Method for Approximate Text String Comparison

  • Sergio Jimenez
  • Claudia Becerra
  • Alexander Gelbukh
  • Fabio Gonzalez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5449)


The Mongue-Elkan method is a general text string comparison method based on an internal character-based similarity measure (e.g. edit distance) combined with a token level (i.e. word level) similarity measure. We propose a generalization of this method based on the notion of the generalized arithmetic mean instead of the simple average used in the expression to calculate the Monge-Elkan method. The experiments carried out with 12 well-known name-matching data sets show that the proposed approach outperforms the original Monge-Elkan method when character-based measures are used to compare tokens.


Edit Distance Word Sense Disambiguation Generalize Arithmetic Recall Curve String Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    De Baets, B., De Meyer, H.: Transitivity-preserving fuzzification schemes for cardinality-based similarity measures. European Journal of Operational Research 160, 726–740 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Baeza-Yates, R., Ribero-Neto, B.: Modern Information Retrieval. Addison Wesley / ACM Press (1999)Google Scholar
  3. 3.
    Bilenko, M., Mooney, R., Cohen, W., Ravikumar, P., Fienberg, S.: Adaptive name matching in information integration. IEEE Intelligent Systems 18 (5), 16–23 (2003)CrossRefGoogle Scholar
  4. 4.
    Bilenko, M., Mooney, R.J.: Adaptive duplicate detection using learnable string similarity measures. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2003)Google Scholar
  5. 5.
    Chaudhuri, S., Ganjam, K., Ganti, V., Motwani, R.: Robust and efficient fuzzy match for online data cleaning. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (2003)Google Scholar
  6. 6.
    Christen, P.: A comparison of personal name matching: Techniques and practical issues. Technical report, The Australian National University, Department of Computer Science, Faculty of Engineering and Information Technology (2006)Google Scholar
  7. 7.
    Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A comparison of string distance metrics for name-matching tasks. In: Proceedings of the IJCAI 2003 Workshop on Information Integration on the Web (2003)Google Scholar
  8. 8.
    Elmagarmid, A.K., Ipeirotis, P.G., Verykios, V.S.: Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering 19(1), 1–16 (2007)CrossRefGoogle Scholar
  9. 9.
    Keskustalo, H., Pirkola, A., Visala, K., Leppänen, E., Järvelin, K.: Non-adjacent digrams improve matching of cross-lingual spelling variants. In: Nascimento, M.A., de Moura, E.S., Oliveira, A.L. (eds.) SPIRE 2003. LNCS, vol. 2857, pp. 252–265. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Kuhn, H.W.: The hungarian method for the assignment problem. Naval Research Logistics Quarterly 2 2, 83–97 (1955)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Levenshtein, V.: Bynary codes capable of correcting deletions, insertions and reversals. Doklady Akademii Nauk SSSR 163(4), 845–848 (1965)zbMATHGoogle Scholar
  12. 12.
    Michelson, M., Knoblock, C.A.: Unsupervised information extraction from unstructured, ungrammatical data sources on the world wide web. International Journal on Document Analysis and Recognition 10(3), 211–226 (2007)CrossRefGoogle Scholar
  13. 13.
    Minton, S.N., Nanjo, C., Knoblock, C.A., Michalowski, M., Michelson, M.: A heterogeneous field matching method for record linkage. In: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)Google Scholar
  14. 14.
    Monge, A.: An adaptive and efficient algorithm for detecting approximately duplicate database records. International Journal on Information Systems Special Issue on Data Extraction, Cleaning, and Reconciliation (2001)Google Scholar
  15. 15.
    Monge, A., Elkan, C.: The field matching problem: Algorithms and applications. In: Proceedings of The Second International Conference on Knowledge Discovery and Data Mining, (KDD) (1996)Google Scholar
  16. 16.
    Moreau, E., Yvon, F., Cappé, O.: Robust similarity measures for named entities matching. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008) (2008)Google Scholar
  17. 17.
    Pedersen, T., Pakhomov, S.V.S., Patwardhan, S., Chute, C.G.: Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics 40(3), 288–299 (2007)CrossRefGoogle Scholar
  18. 18.
    Piskorski, J., Sydow, M.: Usability of string distance metrics for name matching tasks in polish. In: Proceedings of the 3rd Language and Technology Conference, Poznan (2007)Google Scholar
  19. 19.
    Ristad, E.S., Yianilos, P.N.: Learning string edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(5) (1998)Google Scholar
  20. 20.
    Ullmann, J.R.: A binary n-gram technique for automatic correction of substitution deletion, insertion and reversal errors in words. The Computer Journal 20(2), 141–147 (1977)CrossRefzbMATHGoogle Scholar
  21. 21.
    Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. Journal of the Association for Computing Machinery 21(1), 168–173 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Winkler, W., Thibaudeau, Y.: An application fo the fellegi-sunter model of record linkage to the 1990 us decenial census. Technical report, Bureau of the Census, Washington, D.C. (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sergio Jimenez
    • 1
  • Claudia Becerra
    • 1
  • Alexander Gelbukh
    • 2
  • Fabio Gonzalez
    • 1
  1. 1.Intelligent Systems Laboratory (LISI) Systems and Industrial Engineering DepartmentNational University of ColombiaColombia
  2. 2.Natural Language Laboratory Center for Computing Research (CIC)National Polytechnic Institute (IPN)Mexico

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