Identification of FRBR Works Within Bibliographic Databases: An Experiment with UNIMARC and Duplicate Detection Techniques

  • Nuno Freire
  • José Borbinha
  • Pável Calado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4822)

Abstract

Many experiments and studies have been conducted on the application of FRBR as an implementation model for bibliographic databases, in order to improve the services of resource discovery and transmit better perception of the information spaces represented in catalogues. One of these applications is the attempt to identify the FRBR work instances shared by several bibliographic records. In our work we evaluate the applicability to this problem of techniques based on string similarity, used in duplicate detection procedures mainly by the database research community. We describe the particularities of the application of these techniques to bibliographic data, and empirically compare the results obtained with these techniques to those obtained by current techniques, which are based on exact matching. Experiments performed on the Portuguese national union catalogue show a significant improvement over currently used approaches.

Keywords

Functional Requirements for Bibliographic Records FRBR  Bibliographic databases string similarity duplicate detection 

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References

  1. 1.
    IFLA Study Group on the Functional Requirements for Bibliographic Records: Functional requirements for bibliographic records: final report. München: K.G. Saur, UBCIM publications, new series, vol. 19 (1998), www.ifla.org/VII/s13/frbr/frbr.pdf ISBN 3-598-11382-X
  2. 2.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Bilenko, M., Mooney, R.J., Cohen, W.W., Ravikumar, P., Fienberg, S.E.: Adaptive name matching in information integration. IEEE Intelligent Systems 18(5), 16–23 (2003)CrossRefGoogle Scholar
  5. 5.
    Zhao, M.: Semantic matching across heterogeneous data sources. Communications of the ACM 50(1), 45–50 (2007)CrossRefGoogle Scholar
  6. 6.
    Zhao, H., Ram, S.: Entity identification for heterogeneous database integration: A multiple classifier system approach and empirical evaluation. Information Systems 30(2), 119–132 (2005)CrossRefGoogle Scholar
  7. 7.
    Hickey, T.B., O’Neill, E.T., Toves, J.: Experiments with the IFLA Functional Requirements for Bibliographic Records (FRBR). D-Lib Magazine 8, 9 (2002), http://www.dlib.org/dlib/september02/hickey/09hickey.html
  8. 8.
    California Digital Library.: The Melvyl Recommender Project. Full Text Extension. Supplementary Report (2006), http://www.cdlib.org/inside/projects/melvyl_recommender/report_docs/mellon_extension.pdf
  9. 9.
    Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A Comparison of String Distance Metrics for Name-Matching Tasks. American Association for Artificial Intelligence (2003), http://www.isi.edu/info-agents/workshops/ijcai03/papers/Cohen-p.pdf
  10. 10.
    Jaro, M.A.: Advances in record linking methodology as applied to the 1985 census of Tampa Florida. Journal of the American Statistical Society 64, 1183–1210 (1989)Google Scholar
  11. 11.
    Kaiser, M., Lieder, H.J., Majcen, K., Vallant, H.: New Ways of Sharing and Using Authority Information. D-Lib Magazine 9, 11 (2003), http://www.dlib.org/dlib/november03/lieder/11lieder.html
  12. 12.
    Lawrence, S., Giles, C.L., Bollacker, K.D.: Autonomous Citation Matching. In: Proceedings of the Third International Conference on Autonomous Agents, ACM press, New York (1999)Google Scholar
  13. 13.
    Pasula, H., Marthi, B., Milch, B., Russell, S., Shpitser, I.: Identity Uncertainty and Citation Matching. In: Advances in Neural Information Processing (2002), http://people.csail.mit.edu/milch/papers/nipsnewer.pdf
  14. 14.
    Lee, D., On, B.W., Kang, J., Park, S.: Effective and Scalable Solutions for Mixed and Split Citation Problems in Digital Libraries. In: Proceedings of the 2nd international workshop on Information quality in information systems, pp. 69–76 (2005)Google Scholar
  15. 15.
    Aalberg, T.: A process and tool for the conversion of MARC records to a normalized FRBR implementation. Digital Libraries: Achievements, Challenges and Opportunities. In: 9th International Conference on Asian Digital Libraries, pp. 283–292 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Nuno Freire
    • 1
  • José Borbinha
    • 1
  • Pável Calado
    • 1
  1. 1.INESC-ID, Rua Alves Redol 9, Apartado 13069, 1000-029 LisboaPortugal

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