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Capturing Researcher Expertise through MeSH Classification

Authors:

Ross L. CoppelAuthors Info & Claims

K-CAP '15: Proceedings of the 8th International Conference on Knowledge Capture

Article No.: 6, Pages 1 - 8

https://doi.org/10.1145/2815833.2815837

Published: 07 October 2015 Publication History

Abstract

For a large research institution and a broad research discipline such as the life sciences, it is a highly important and very challenging task to capture each researcher's expertise, and to match researchers by expertise to assist in identifying inter-disciplinary collaboration opportunities and in making informed policy decisions. The challenges are multi-dimensional, stemming from the needs to (a) provide thorough coverage of the breadth and depth of the disciplinary areas, (b) develop accurate representation of researcher's expertise, and (c) process large volumes of data efficiently. Medical Subject Headings (MeSH), a comprehensive taxonomy for the life sciences, has been widely used for indexing MEDLINE publications. In this paper, we present a novel framework for capturing and matching research expertise based on knowledge encoded in MeSH. Specifically, (1) we design a novel and effective hybrid MeSH classification algorithm by combining state-of-the-art methods, and (2) using MeSH terms aggregated from a researcher's publications, we design a researcher matching algorithm based on semantic similarity that takes into consideration the structure of the MeSH taxonomy.

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Cited By

Osuna FAkbar MGates A(2017)On Using Disparate Scholarly Data to Identify Potential Members for Interdisciplinary Research Groups2017 IEEE International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2017.33(59-68)Online publication date: 4-Aug-2017
https://dl.acm.org/doi/10.1109/IRI.2017.33

Index Terms

Capturing Researcher Expertise through MeSH Classification
1. Computing methodologies
  1. Machine learning
    1. Learning settings
2. Information systems
  1. Information retrieval
    1. Document representation

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Published In

cover image ACM Other conferences

K-CAP '15: Proceedings of the 8th International Conference on Knowledge Capture

October 2015

209 pages

ISBN:9781450338493

DOI:10.1145/2815833

Conference Chair:
Ken Barker
IBM Watson Research, USA
,
Program Chair:
José Manuel Gómez-Pérez
Expert System, Spain

Copyright © 2015 ACM.

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 October 2015

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K-CAP 2015

K-CAP 2015: Knowledge Capture Conference

October 7 - 10, 2015

NY, Palisades, USA

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K-CAP '15 Paper Acceptance Rate 16 of 56 submissions, 29%;

Overall Acceptance Rate 55 of 198 submissions, 28%

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Cited By

Osuna FAkbar MGates A(2017)On Using Disparate Scholarly Data to Identify Potential Members for Interdisciplinary Research Groups2017 IEEE International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2017.33(59-68)Online publication date: 4-Aug-2017
https://dl.acm.org/doi/10.1109/IRI.2017.33

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