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Population Reconstruction pp 61–83Cite as

Learning Name Variants from Inexact High-Confidence Matches

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Abstract

Name variants which differ more than a few characters can seriously hamper record linkage. A method is described by which variants of first names and surnames can be learned automatically from records that contain more information than needed for a true link decision. Post-processing and limited manual intervention (active learning) is unavoidable, however, to differentiate errors in the original and the digitised data from variants. The method is demonstrated on the basis of an analysis of 14.8 million records from the Dutch vital registration.

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Notes

  1. 1.

    A web-based query interface is available on https://familysearch.org/stdfinder/NameStandardLookup.jsp.

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Acknowledgments

This work is part of the research programme LINKS (LINKing System for historical family reconstruction, http://www.iisg.nl/hsn/projects/links.html), which is financed by the Netherlands Organisation for Scientific Research (NWO), grant 640.004.804.

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Authors and Affiliations

  1. Utrecht Institute of Linguistics-OTS, Utrecht University, Trans 10, 3512 JK, Utrecht, The Netherlands

    Gerrit Bloothooft

  2. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Marijn Schraagen

Authors
  1. Gerrit Bloothooft
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  2. Marijn Schraagen
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Corresponding author

Correspondence to Gerrit Bloothooft .

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Editors and Affiliations

  1. Utrecht Universty, Utrecht, The Netherlands

    Gerrit Bloothooft

  2. The Australian National University, Canberra, Aust Capital Terr, Australia

    Peter Christen

  3. International Inst. of Social History, Amsterdam, The Netherlands

    Kees Mandemakers

  4. Leiden University, Leiden, The Netherlands

    Marijn Schraagen

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Bloothooft, G., Schraagen, M. (2015). Learning Name Variants from Inexact High-Confidence Matches. In: Bloothooft, G., Christen, P., Mandemakers, K., Schraagen, M. (eds) Population Reconstruction. Springer, Cham. https://doi.org/10.1007/978-3-319-19884-2_4

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