Discovery and visualization of expertise in a scientific community

Abstract

In numerous contexts and environments, it is necessary to identify and assign (potential) experts to subject fields. In the context of an academic journal for computer science (J.UCS), papers and reviewers are classified using the ACM classification scheme. This paper describes a system to identify and present potential reviewers for each category from the entire body of paper's authors. The topical classification hierarchy is visualized as a hyperbolic tree and currently assigned reviewers are listed for a selected node (computer science category). In addition, a spiral visualization is used to overlay a ranked list of further potential reviewers (high-profile authors) around the currently selected category. This new interface eases the task of journal editors in finding and assigning reviewers. The system is also useful for users who want to find research collaborators in specific research areas.

Constructing experts profiles from Linked Open Data

“…Cameron [15] employed publication impact as an additional measure for expertise mining. In our previous work [2], we employed number of factors as facets for expertise mining. We used multi-faceted approach where one can rank expert more comprehensively.…”

“…Subsequently, all weights were added to get an overall ranking of experts in the area. Further detail can be found in [2].…”

“…The experts of J.UCS are visualized using hyperbolic visualization [2]. In the current work, hyperlink for all the discovered experts is generated.…”

“…Generally, the experts are discovered using number of publication [1]. However, in our previous work, we employed a multi-faceted approach where the experts are discovered by looking on the number of publications of an individual, quality of publications, citation record, and experience [2].…”