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Using clustering strategies for creating authority files

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  • James C. French
  • Allison L. Powell
  • Eric Schulman

Abstract

As more online databases are integrated into digital libraries, the issue of quality control of the data becomes increasingly important, especially as it relates to the effective retrieval of information. Authority work, the need to discover and reconcile variant forms of strings in bibliographic entries, will become more critical in the future. Spelling variants, misspellings, and transliteration differences will all increase the difficulty of retrieving information. We investigate a number of approximate string matching techniques that have traditionally been used to help with this problem. We then introduce the notion of approximate word matching and show how it can be used to improve detection and categorization of variant forms. We demonstrate the utility of these approaches using data from the Astrophysics Data System and show how we can reduce the human effort involved in the creation of authority files.

Suggested Citation

  • James C. French & Allison L. Powell & Eric Schulman, 2000. "Using clustering strategies for creating authority files," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(8), pages 774-786.
  • Handle: RePEc:bla:jamest:v:51:y:2000:i:8:p:774-786
    DOI: 10.1002/(SICI)1097-4571(2000)51:83.0.CO;2-P
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    Cited by:

    1. Carmen Galvez & Félix Moya-Anegón, 2007. "Standardizing formats of corporate source data," Scientometrics, Springer;Akadémiai Kiadó, vol. 70(1), pages 3-26, January.
    2. Yongwen Huang & Jiao Li & Tan Sun & Guojian Xian, 2020. "Institution information specification and correlation based on institutional PIDs and IND tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 381-396, January.
    3. Shuiqing Huang & Bo Yang & Sulan Yan & Ronald Rousseau, 2014. "Institution name disambiguation for research assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 823-838, June.
    4. Pascal Cuxac & Jean-Charles Lamirel & Valerie Bonvallot, 2013. "Efficient supervised and semi-supervised approaches for affiliations disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(1), pages 47-58, October.

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