Describing Papers and Reviewers' Competences by Taxonomy of Keywords
This work addresses the challenge of fair and accurate reviewer assignment in academic peer review, but it is incremental as it adapts existing similarity measures.
The paper tackles the problem of automatically assigning reviewers to papers by proposing a method that uses a taxonomy of keywords to describe papers and reviewers' competences, enabling similarity measures to account for semantic closeness even without exact keyword matches, and reports that this approach allows non-zero similarity factors to be accurately calculated.
This article focuses on the importance of the precise calculation of similarity factors between papers and reviewers for performing a fair and accurate automatic assignment of reviewers to papers. It suggests that papers and reviewers' competences should be described by taxonomy of keywords so that the implied hierarchical structure allows similarity measures to take into account not only the number of exactly matching keywords, but in case of non-matching ones to calculate how semantically close they are. The paper also suggests a similarity measure derived from the well-known and widely-used Dice's coefficient, but adapted in a way it could be also applied between sets whose elements are semantically related to each other (as concepts in taxonomy are). It allows a non-zero similarity factor to be accurately calculated between a paper and a reviewer even if they do not share any keyword in common.