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A probabilistic similarity metric for medline records: A model for author name disambiguation (2005)
| Content Provider | CiteSeerX |
|---|---|
| Author | Torvik, Vetle I. Weeber, Marc Swanson, Don R. Smalheiser, Neil R. |
| Abstract | 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”: |
| File Format | |
| Volume Number | 56 |
| Journal | Journal of the American Society for Information Science and Technology |
| Language | English |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Author Name Disambiguation Probabilistic Similarity Medline Record Powerful Similarity Profile Author Match Versus Nonmatches Name Attribute Medical Subject Heading Reference Set Small Proportion Last Name Match Set Different Medline Article Coauthor Name Unbiased Manner Public Service Author Name Journal Name Similarity Profile Distribution |
| Content Type | Text |
| Resource Type | Article |
