Document (#28307)

Author
Torvik, V.I.
Weeber, M.
Swanson, D.R.
Smalheiser, N.R.
Title
¬A probabilistic similarity metric for medline mecords : a model for author name disambiguation
Source
Journal of the American Society for Information Science and Technology. 56(2005) no.2, S.140-158
Year
2005
Abstract
We present a model for estimating the probability that a pair of author names (sharing last name and first initial), appearing an two different Medline articles, refer to the same individual. The model uses a simple yet powerful similarity profile between a pair of articles, based an 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 an 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.
Theme
Informetrie
Field
Medizin
Object
Medline

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  5. Kim, J.; Kim, J.; Owen-Smith, J.: Ethnicity-based name partitioning for author name disambiguation using supervised machine learning (2021) 0.14
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