  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.
