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A network approach to expertise retrieval based on path similarity and credit allocation

Xiancheng Li, Luca Verginer, Massimo Riccaboni and P. Panzarasa ()
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Xiancheng Li: Queen Mary University of London
Massimo Riccaboni: Axes Research Unit, IMT School for Advanced Studies
P. Panzarasa: Queen Mary University of London

Journal of Economic Interaction and Coordination, 2022, vol. 17, issue 2, No 5, 533 pages

Abstract: Abstract With the increasing availability of online scholarly databases, publication records can be easily extracted and analysed. Researchers can promptly keep abreast of others’ scientific production and, in principle, can select new collaborators and build new research teams. A critical factor one should consider when contemplating new potential collaborations is the possibility of unambiguously defining the expertise of other researchers. While some organisations have established database systems to enable their members to manually produce a profile, maintaining such systems is time-consuming and costly. Therefore, there has been a growing interest in retrieving expertise through automated approaches. Indeed, the identification of researchers’ expertise is of great value in many applications, such as identifying qualified experts to supervise new researchers, assigning manuscripts to reviewers, and forming a qualified team. Here, we propose a network-based approach to the construction of authors’ expertise profiles. Using the MEDLINE corpus as an example, we show that our method can be applied to a number of widely used data sets and outperforms other methods traditionally used for expertise identification.

Keywords: Expertise retrieval; Path similarity; Credit allocation; Heterogeneous information networks (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11403-020-00315-0

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