Lee, DH and Schleyer, T
(2010)
MeSH term explosion and author rank improve expert recommendations.
In: UNSPECIFIED.
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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Item Type: |
Conference or Workshop Item
(UNSPECIFIED)
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Status: |
Published |
Creators/Authors: |
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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: |
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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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