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On the Problem of Identifying the Quality of Geographic Metadata

  • Rafael Tolosana-Calasanz
  • José A. Álvarez-Robles
  • Javier Lacasta
  • Javier Nogueras-Iso
  • Pedro R. Muro-Medrano
  • F. Javier Zarazaga-Soria
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4172)

Abstract

Geographic metadata quality is one of the most important aspects on the performance of Geographic Digital Libraries. After reviewing previous attempts outside the geographic domain, this paper presents early results from a series of experiments for the development of a quantitative method for quality assessment. The methodology is developed through two phases. Firstly, a list of geographic quality criteria is compiled from several experts of the area. Secondly, a statistical analysis (by developing a Principal Component Analysis) of a selection of geographic metadata record sets is performed in order to discover the features which correlate with good geographic metadata.

Keywords

Spatial Data Infrastructure Entity Naming Eprint Archive Geographic Domain Metadata Record 
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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References

  1. 1.
    Barton, J., Currier, S., Hey, J.: Building Quality Assurance into Metadata Creation: an Analysis based on the Learning Objects and e-Prints Communities of Practice. In: Proceedings of the 2003 Dublin Core Conference: Supporting Communities of Discourse and Practice-Metadata Research and Applications (2003) ISBN 0-9745303-0-1Google Scholar
  2. 2.
    Holden, C.: From Local Challenges to a Global Community: Learning Repositories and the Global Learning Repositories Summit. The Academic ADL Co-Lab, Version 1.0 (2003)Google Scholar
  3. 3.
    Guy, M., Powell, A., Day, M.: Improving the Quality of Metadata in Eprint Archives. Ariadne Magazine (38) (2004), http://www.ariadne.ac.uk/
  4. 4.
    Federal Geographic Data Committee (FGDC): Content Standard for Digital Geospatial Metadata, version 2.0. Document FGDC-STD-001-1998. Technical report (1998)Google Scholar
  5. 5.
    International Organization for Standardization (ISO): Geographic information - Metadata. ISO 19115:2003 (2003)Google Scholar
  6. 6.
    Armento, B., Terveen, L., Hill, W.: Predicting expert quality ratings of Web documents. In: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval. Does ”authority” mean quality?, Athens, Greece, pp. 296–303 (2000) ISBN 1-58113-226Google Scholar
  7. 7.
    Custard, M., Summer, T.: Using Machine Learning to Support Quality Judgements. D-Lib Magazine 11(10) (2005) ISSN 1082-9873Google Scholar
  8. 8.
    Hughes, B.: Metadata Quality Evaluation: Experience from the Open Language Archives Community. In: Chen, Z., et al. (eds.) ICADL 2004. LNCS, vol. 3334, pp. 320–329. Springer, Heidelberg (2004) ISBN 3-540-24030-6CrossRefGoogle Scholar
  9. 9.
    International Organization for Standardization (ISO): Information and documentation - The Dublin Core metadata element set. ISO 15836:2003 (2003)Google Scholar
  10. 10.
    Zhang, B., Gonçalves, M.A., Fox, E.A.: An OAI-Based Filtering Service for CITIDEL from NDLTD. In: Sembok, T.M.T., Zaman, H.B., Chen, H., Urs, S.R., Myaeng, S.-H. (eds.) ICADL 2003. LNCS, vol. 2911, pp. 590–601. Springer, Heidelberg (2003) ISBN 3-540-20608-6CrossRefGoogle Scholar
  11. 11.
    Nogueras-Iso, J., Zarazaga-Soria, F.J., Lacasta, J., Béjar, R., Muro-Medrano, P.R.: Metadata Standard Interoperability: Application in the Geographic Information Domain. Computers, Environment and Urban Systems 28(6), 611–634 (2004)CrossRefGoogle Scholar
  12. 12.
    Nogueras-Iso, J., Zarazaga-Soria, F.J., Muro-Medrano, P.R.: Geographic Information Metadata for Spatial Data Infrastructures - Resources, Interoperability and Information Retrieval. Springer, Heidelberg (2005) ISBN 3-540-24464-6Google Scholar
  13. 13.
    Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer Series in Statistics. Springer, Heidelberg (2002)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rafael Tolosana-Calasanz
    • 1
  • José A. Álvarez-Robles
    • 1
  • Javier Lacasta
    • 1
  • Javier Nogueras-Iso
    • 1
  • Pedro R. Muro-Medrano
    • 1
  • F. Javier Zarazaga-Soria
    • 1
  1. 1.Computer Science and Systems Engineering DepartmentUniversity of ZaragozaZaragozaSpain

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