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MeSH term explosion and author rank improve expert recommendations

Lee, DH and Schleyer, T (2010) MeSH term explosion and author rank improve expert recommendations. In: UNSPECIFIED.

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Abstract

Information overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Lee, DHhyl12@pitt.eduHYL12
Schleyer, Ttitus@pitt.eduTITUS0000-0003-1829-971X
Date: 1 January 2010
Date Type: Publication
Journal or Publication Title: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume: 2010
Page Range: 412 - 416
Event Type: Conference
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Related URLs:
Other ID: ISBN: 0-9647743-9-9, ISSN: 1559-4076
Date Deposited: 06 Jul 2011 15:54
Last Modified: 02 Aug 2022 17:44
URI: http://d-scholarship.pitt.edu/id/eprint/5977

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