A probabilistic similarity metric for Medline records: A model for author name disambiguation
Article first published online: 5 NOV 2004
Copyright © 2004 Wiley Periodicals, Inc.
Journal of the American Society for Information Science and Technology
Volume 56, Issue 2, pages 140–158, 15 January 2005
How to Cite
Torvik, V. I., Weeber, M., Swanson, D. R. and Smalheiser, N. R. (2005), A probabilistic similarity metric for Medline records: A model for author name disambiguation. J. Am. Soc. Inf. Sci., 56: 140–158. doi: 10.1002/asi.20105
- Issue published online: 15 DEC 2004
- Article first published online: 5 NOV 2004
- Manuscript Accepted: 11 FEB 2004
- Manuscript Revised: 16 OCT 2003
- Manuscript Received: 24 APR 2003
We present a model for estimating the probability that a pair of author names (sharing last name and first initial), appearing on two different Medline articles, refer to the same individual. The model uses a simple yet powerful similarity profile between a pair of articles, based on title, journal name, coauthor names, medical subject headings (MeSH), language, affiliation, and name attributes (prevalence in the literature, middle initial, and suffix). The similarity profile distribution is computed from reference sets consisting of pairs of articles containing almost exclusively author matches versus nonmatches, generated in an unbiased manner. Although the match set is generated automatically and might contain a small proportion of nonmatches, the model is quite robust against contamination with nonmatches. We have created a free, public service (“Author-ity”: http://arrowsmith.psych.uic.edu) that takes as input an author's name given on a specific article, and gives as output a list of all articles with that (last name, first initial) ranked by decreasing similarity, with match probability indicated.