A probabilistic similarity metric for medline records: A model for author name disambiguation

Vetle I. Torvik, Marc Weeber, Don R. Swanson, Neil R. Smalheiser

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)140-158
Number of pages19
JournalJournal of the American Society for Information Science and Technology
Issue number2
StatePublished - Jan 15 2005
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence

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