Multimedia Tools and Applications

, Volume 68, Issue 3, pp 845–861 | Cite as

Variable linkage for multimedia metadata schema matching

  • Jordi Nin
  • Ruben Tous
  • Jaime Delgado


Today there are many media sharing applications that use diverse metadata formats to describe media resources. This leads to interoperability issues in cataloguing, searching and annotation. This situation poses schema matching algorithms in the eye of the storm of metadata interoperability. In this paper we present two different solutions for multimedia metadata schema matching using variable linkage algorithms. These methods consist in directly comparing the data values stored in the different metadata variables, allowing to overcome the inherent limitations of schema-level matching approaches. We show the feasibility of these methods through some experiments with real metadata information extracted from the image hosting websites Deviantart, Flickr and Picasa.


Metadata integration Image tagging Variable integration Schema matching Record linkage 



This work has been partly supported by the Spanish government TEC2008-06692-C02-01 and ARES CONSOLIDER INGENIO 2010 CSD2007-00004.


  1. 1.
    Berlin J, Motro A (2002) Database schema matching using machine learning with feature selection. In: 14th int. Conf. of Advanced Information Systems Engineering (CAiSE). Lecture notes in computer science, vol 2348, pp 452–466Google Scholar
  2. 2.
    Bouyssou D, Marchant T, Pirlot M, Tsoukias A, Vincke P (2011) Evaluation and decision models with multiple criteria: stepping stones for the analyst. In: International series in operations research & management science, vol 86. SpringerGoogle Scholar
  3. 3.
    Damerau FJ (1964) A technique for computer detection and correction of spelling errors. Commun ACM 7(3):171–176CrossRefGoogle Scholar
  4. 4.
    Doeller M, Stegmaier F, Kosch H, Tous R, Delgado J (2010) Standardized interoperable image retrieval. In: Proceedings of the 2010 ACM symposium on applied computing. SAC ’10. ACM, New York, pp 880–886CrossRefGoogle Scholar
  5. 5.
    Elmagarmid AK, Ipeirotis PG, Verykios VS (2007) Duplicate record detection: a survey. IEEE Trans Knowl Data Eng (TKDE) 19(1):1–16CrossRefGoogle Scholar
  6. 6.
    Euzenat J, Shvaiko P (2007) Ontology matching. Database management & information retrieval. SpringerGoogle Scholar
  7. 7.
    Herranz J, Nin J, Solé M (2011) Optimal symbol alignment distance: a new distance for sequences of symbols. IEEE Trans Knowl Data Eng (TKDE) 23(10):1541–1554CrossRefGoogle Scholar
  8. 8.
    Jaro M (1989) Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. J Am Stat Assoc 84:414–420CrossRefGoogle Scholar
  9. 9.
    Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions, and reversals. Sov Phys Dokl 10:707–710. MathSciNetGoogle Scholar
  10. 10.
    Mahalanobis P (1936) On the generalised distance in statistics. In: Proceedings of the national institute of sciences of India, vol 2, pp 49–55Google Scholar
  11. 11.
    Mitchell T (1997) Machine learning. McGraw-HillGoogle Scholar
  12. 12.
    Nin J, Torra V (2009) Towards the evaluation of time series protection methods. Inf Sci 179(11):1663–1677CrossRefzbMATHGoogle Scholar
  13. 13.
    Rijsbergen C (1979) Information retrieval. ButterworthGoogle Scholar
  14. 14.
    Rubin D (1976) Inference and missing data. Biometrika 63:581–590CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Seligman L, Mork P, Halevy A, Smith K, Carey M, Chen K, Wolf C, Madhavan J, Kannan A, Burdick D (2010) Openii: an open source information integration toolkit. In: ACM int. conf. on management of data (SIGMOD), pp 1057–1059Google Scholar
  16. 16.
    Shvaiko P, Euzenat J (2005) A survey of schema-based matching approaches. J Data Semantics IV, LNCS 3730:146–171CrossRefGoogle Scholar
  17. 17.
    Torra V (2004) OWA operators in data modeling and reidentification. IEEE Trans Fuzzy Syst 12(5):652–660CrossRefGoogle Scholar
  18. 18.
    Torra V, Narukawa Y (2007) Modeling decisions: information fusion and aggregation operators. SpringerGoogle Scholar
  19. 19.
    Torra V, Nin J (2008) Record linkage for database integration using fuzzy integrals. Int J Intell Syst (IJIS) 23(6):715–734CrossRefzbMATHGoogle Scholar
  20. 20.
    Tous R, Delgado J (2009) A lego-like metadata architecture for image search & retrieval. In: Proceedings of the 2009 20th international workshop on database and expert systems application, pp 246–250Google Scholar
  21. 21.
    Tous R, Nin J, Delgado J (2011) Approaches and standards for metadata interoperability in distributed image search&retrieval. In: 22nd Int. conf. on database and expert systems applications. Lecture notes in computer science, vol 6861. Springer, pp 234–248Google Scholar
  22. 22.
    Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Morgan KaufmannGoogle Scholar
  23. 23.
    Yager R (1988) On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans Syst Man Cybern 18:183–190CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Departament d’Arquitectura de ComputadorsUniversitat Politècnica de CatalunyaBarcelonaSpain

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