Domain expertise extraction for finding rising stars

Zhu, Lin and Zhang, Junjie and Cunningham, Scott W. (2022) Domain expertise extraction for finding rising stars. Scientometrics, 127 (9). pp. 5475-5495. ISSN 0138-9130 (

[thumbnail of Zhu-etal-Scientometrics-2022-Domain-expertise-extraction-for-finding-rising-stars]
Text. Filename: Zhu_etal_Scientometrics_2022_Domain_expertise_extraction_for_finding_rising_stars.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (1MB)| Preview


The field of expertise extraction utilizes published research enabling communities to highlight and identify the skills of researchers within specific scientific domains. This can be useful for evaluating research performance, and in the case of rising stars, in identifying top scientific talent. Previous research has harvested a range of publication indicators in an effort to identify expertise and talent. These include content indicators, citation metrics, and also the position of a researcher within a full collaboration network of scientists. The existing mechanism of expertise extraction utilizes all papers attributed to a scientific author, thereby potentially neglecting their specific or specialized expertise. Here we show that a tensor decomposition technique when applied to the problem addresses a number of useful problems. This includes better identification of individual expertise, as well as an integrated appraisal of an author’s role in an extended scientific network. The technique will afford new analyses of knowledge production which consider specialisation and diversity as core elements for further analysis. More generally the tensor decomposition techniques presented in this paper can be applied to a range of scientometric problems where multi-modal data is encountered.