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MaSiMe: A Customized Similarity Measure and Its Application for Tag Cloud Refactoring

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 5872)

Abstract

Nowadays the popularity of tag clouds in websites is increased notably, but its generation is criticized because its lack of control causes it to be more likely to produce inconsistent and redundant results. It is well known that if tags are freely chosen (instead of taken from a given set of terms), synonyms (multiple tags for the same meaning), normalization of words and even, heterogeneity of users are likely to arise, lowering the efficiency of content indexing and searching contents. To solve this problem, we have designed the Maximum Similarity Measure (MaSiMe) a dynamic and flexible similarity measure that is able to take into account and optimize several considerations of the user who wishes to obtain a free-of-redundancies tag cloud. Moreover, we include an algorithm to effectively compute the measure and a parametric study to determine the best configuration for this algorithm.

Keywords

  • social tagging systems
  • social network analysis
  • Web 2.0

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References

  1. Marinchev, I.: Practical Semantic Web Tagging and Tag Clouds. Cybernetics and Information Technologies 6(3), 33–39 (2006)

    Google Scholar 

  2. Kiefer, C., Bernstein, A., Stocker, M.: The Fundamentals of iSPARQL: A Virtual Triple Approach for Similarity-Based Semantic Web Tasks. 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. 295–309. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  3. Echarte, F., Astrain, J.J., Córdoba, A., Villadangos, J.: Pattern Matching Techniques to Identify Syntactic Variations of Tags in Folksonomies. In: Lytras, M.D., Carroll, J.M., Damiani, E., Tennyson, R.D. (eds.) WSKS 2008. LNCS (LNAI), vol. 5288, pp. 557–564. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  4. Widdows, D.: Geometry and Meaning. The University of Chicago Press (2004)

    Google Scholar 

  5. Levenshtein, V.: Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics-Doklady 10, 707–710 (1966)

    MathSciNet  Google Scholar 

  6. Ziegler, P., Kiefer, C., Sturm, C., Dittrich, K.R., Bernstein, A.: Detecting Similarities in Ontologies with the SOQA-SimPack Toolkit. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 59–76. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  7. Ukkonen, E.: Approximate String Matching with q-grams and Maximal Matches. Theor. Comput. Sci. 92(1), 191–211 (1992)

    MATH  CrossRef  MathSciNet  Google Scholar 

  8. Knuth, D.: The Art of Computer Programming. Fundamental Algorithms, 3rd edn., vol. 1. Addison-Wesley, Reading (1997)

    Google Scholar 

  9. Cilibrasi, R., Vitányi, P.M.B.: The Google Similarity Distance. IEEE Trans. Knowl. Data Eng. 19(3), 370–383 (2007)

    CrossRef  Google Scholar 

  10. Stoilos, G., Stamou, G.B., Kollias, S.D.: A String Metric for Ontology Alignment. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 624–637. Springer, Heidelberg (2005)

    CrossRef  Google Scholar 

  11. http://www.aifb.uni-karlsruhe.de/WBS/meh/foam/ontologies/russia1.owl (last visit: February 3, 2009)

  12. http://www.aifb.uni-karlsruhe.de/WBS/meh/foam/ontologies/russia2.owl (last visit: February 3, 2009)

  13. Ehrig, M., Sure, Y.: FOAM - Framework for Ontology Alignment and Mapping - Results of the Ontology Alignment Evaluation Initiative. Integrating Ontologies (2005)

    Google Scholar 

  14. Li, Y., Li, J., Zhang, D., Tang, J.: Result of Ontology Alignment with RiMOM at OAEI 2006. In: International Workshop on Ontology Matching collocated with the 5th International Semantic Web Conference (2006)

    Google Scholar 

  15. Specia, L., Motta, E.: Integrating Folksonomies with the Semantic Web. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 624–639. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

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Urdiales-Nieto, D., Martinez-Gil, J., Aldana-Montes, J.F. (2009). MaSiMe: A Customized Similarity Measure and Its Application for Tag Cloud Refactoring. In: Meersman, R., Herrero, P., Dillon, T. (eds) On the Move to Meaningful Internet Systems: OTM 2009 Workshops. OTM 2009. Lecture Notes in Computer Science, vol 5872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05290-3_112

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  • DOI: https://doi.org/10.1007/978-3-642-05290-3_112

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05289-7

  • Online ISBN: 978-3-642-05290-3

  • eBook Packages: Computer ScienceComputer Science (R0)