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Towards the automation of address identification

Author

Listed:
  • Fernanda Morillo

    (Spanish National Research Council (CSIC))

  • Javier Aparicio

    (Spanish National Research Council (CSIC))

  • Borja González-Albo

    (Spanish National Research Council (CSIC))

  • Luz Moreno

    (Spanish National Research Council (CSIC))

Abstract

A new semi-automatic method is presented to standardize or codify addresses, in order to produce bibliometric indicators from bibliographic databases. The hypothesis is that this new method is very trustworthy to normalize authors’ addresses, easy and quick to obtain. As a way to test the method, a set of already hand-coded data is chosen to verify its reliability: 136,821 Spanish documents (2006–2008) downloaded previously from the Web of Science database. Unique addresses from this set were selected to produce a list of keywords representing various institutional sectors. Once the list of terms is obtained, addresses are standardized with this information and the result is compared to the previous hand-coded data. Some tests are done to analyze possible association between both systems (automatic and hand-coding), calculating measures of recall and precision, and some statistical directional and symmetric measures. The outcome shows a good relation between both methods. Although these results are quite general, this overview of institutional sectors is a good way to develop a second approach for the selection of particular centers. This system has some new features because it provides a method based on the previous non-existence of master lists or tables and it has a certain impact on the automation of tasks. The validity of the hypothesis has been proved taking into account not only the statistical measures, but also considering that the obtaining of general and detailed scientific output is less time-consuming and will be even less due to the feedback of these master tables reused for the same kind of data. The same method could be used with any country and/or database creating a new master list taking into account their specific characteristics.

Suggested Citation

  • Fernanda Morillo & Javier Aparicio & Borja González-Albo & Luz Moreno, 2013. "Towards the automation of address identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 207-224, January.
    • Handle: RePEc:spr:scient:v:94:y:2013:i:1:d:10.1007_s11192-012-0733-6
    DOI: 10.1007/s11192-012-0733-6
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    References listed on IDEAS

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    Cited by:

    1. Fernanda Morillo & Belén Álvarez-Bornstein, 2018. "How to automatically identify major research sponsors selecting keywords from the WoS Funding Agency field," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1755-1770, December.
    2. Fernanda Morillo & Ignacio Santabárbara & Javier Aparicio, 2013. "The automatic normalisation challenge: detailed addresses identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 953-966, June.
    3. Fernanda Morillo & Preiddy Efrain-Garcia, 2015. "A bibliometric analysis of Technology Centres," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 685-713, September.
    4. 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.

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