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The unification of institutional addresses applying parametrized finite-state graphs (P-FSG)

Author

Listed:
  • Carmen Galvez

    (Scimago Research Group, Department of Information Science,University of Granada)

  • Félix Moya-Anegón

    (Scimago Research Group, Department of Information Science,University of Granada)

Abstract

Summary We propose a semi-automatic method based on finite-state techniques for the unification of corporate source data, with potential applications for bibliometric purposes. Bibliographic and citation databases have a well-known problem of inconsistency in the data at micro-level and meso-level, affecting the quality of bibliometric searches and the evaluation of research performance. The unification method applies parametrized finite-state graphs (P-FSG) and involves three stages: (1) breaking of corporate source data in independent units of analysis; (2) creation of binary matrices; and (3) drawing finite-state graphs. This procedure was tested on university departmental addresses, downloaded from the ISI Web of Science. Evaluation was in terms of an adaptation of the measures of precision and recall. The results demonstrate the usefulness of this approach, though it requires some human processing.

Suggested Citation

  • Carmen Galvez & Félix Moya-Anegón, 2006. "The unification of institutional addresses applying parametrized finite-state graphs (P-FSG)," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(2), pages 323-345, November.
    • Handle: RePEc:spr:scient:v:69:y:2006:i:2:d:10.1007_s11192-006-0156-3
    DOI: 10.1007/s11192-006-0156-3
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Carmen Galvez & Félix Moya-Anegón, 2007. "Standardizing formats of corporate source data," Scientometrics, Springer;Akadémiai Kiadó, vol. 70(1), pages 3-26, January.
    4. 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.
    5. Zehra Taşkın & Umut Al, 2014. "Standardization problem of author affiliations in citation indexes," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 347-368, January.
    6. Sjoerd Hardeman, 2013. "Organization level research in scientometrics: a plea for an explicit pragmatic approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1175-1194, March.
    7. Omar Hernando Avila-Poveda, 2014. "Technical report: the trend of author compound names and its implications for authorship identity identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 833-846, October.

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