Author Name Disambiguation in Medline

1
Author Name Disambiguation in Medline

• Vetle I. Torvik and
• Neil R. Smalheiser
• August 31, 2006

2
(No Transcript)
3
(No Transcript)
4
A Statistically Based Model

• Hypothesis an individual tends to publish papers
  with similar attributes, sufficiently so that
  these attributes suffice to disambiguate the
  authors.
• The case of Dr. Tom Jobe?
• Large, automatically generated training sets of
  pairs of articles matching (Last Name, First
  Initial) written by the same person vs. by
  different individuals.

5
Name Attributes

• Suffix if present (III, Jr.)
• Middle initial match
• Original model got very good performance without
  using first names at all!
• First name (if available in Medline, or if it can
  be scraped from online papers)
• Name spelling variants
• Name frequency

6
Article Attributes

• Journal name
• Number of co-author names in common
• Affiliation words in common
• may not be given for all co-authors
• Name correlations with affiliations (e.g. Ito and
  Japan are correlated)
• Language of the article
• Title words in common
• Email addresses, if given
• assign to right author
• MeSH headings in common

7
A Monotone Model

• Each pair of papers creates a vector of 10
  dimensions, each of which has a matching score.
• Assume monotonicity more attributes in common,
  more likely written by the same person
• Allows for nonlinear and interactive effects
  across dimensions

8
Estimate Pairwise Frequencies

• For a given pair of articles, compute the match
  vector, then look up its frequencies in pos vs.
  neg training sets ratio R value
• For a given name, estimate the a priori
  probability P that any two papers will be written
  by the same person
• This is a whole story in itself.
• 1/1 (1-P)/PR probability of a match
• The Author-ity Site at (http//arrowsmith.psych.ui
  c.edu)

9
Beyond Pairwise Comparisons

• A and B share titles, journals
• B and C share co-authors, affiliations
• But A and C share nothing!
• Yet p(AC) must be gt (p(AB) p(BC) -1)
• Triangle inequality using probabilities, detect
  and correct anomalies
• due to missing data or higher order correlations
• Catch un-characteristic papers by an author
• Another long story to optimize the methods!

10
Clustering all papers in Medline according to
author-individuals

• First we compute all pairwise probabilities for
  each (last name, first initial) modified with
  triplet correction
• Then we form clusters
• at p 0.95 (high precision)
• and at p 0.5 (high recall)
• i.e. the chance is greater than 0.5 that it
  belongs to some cluster, or it stays as a
  singleton

11
First-Pass Disambiguation is Complete!

• Except for several hundred names having more than
  3000 papers each,
• reach memory limit,
• will assess if the model is reliable for the
  biggest names
• For now, proceed for papers giving first names.
• Monitoring for over-clustering and
  under-clustering
• Summarizing global statistics

12
Immediate Next Steps

• Evaluate the clustering performance
• Old vs. new papers
• Importance of missing data
• Very frequent names
• Singletons, least confident assignments
• Update the web interface

13
(No Transcript)
14
(No Transcript)
15
(No Transcript)
16
Upcoming Grant RenewalAim 1 Special Cases

• name reversal, hyphenated names, spelling errors,
  Gerald vs. Jerry, Rick vs. A. Rick
• Use co-author assignment to help disambiguate
  another co-author
• Compute confidence level of assignment for each
  paper, identify least confident assignments

17
Upcoming Grant RenewalAim 2 Update the Model

• Original model covers 1966-present, but new
  papers have different information, MeSH, emails,
  online information
• Modify training sets with recent papers.
• Journal name partial match
• Abstract words match?
• Affiliations matched to each authors in PMC,
  online papers
• References Cited information taken from PMC

18
Upcoming Grant RenewalAim 3 Web Interface

• Update the pairwise interface (given name, a
  particular paper, list all others in order of
  match probability)
• Show clusters given a name, show all clusters
  of author-individuals, link to Community of
  Science, searchable by attributes, can summarize
  and explore further (Anne OTate tool)
• Author profile/collaboration finder tools
• Data made available for bibliometrics and
  collaboration network research

19
Upcoming Grant RenewalAim 4 Curation

• Curator to identify errors and least-confident
  assignments
• manually
• machine methods (e.g. wobble in clustering)
• change the database and alter the model as needed
  
• Wiki Authors will monitor postings to Wiki and
  change the database as verified and warranted
  (e.g. maiden name to married name)