In the Human Resources domain the accurate matching between job
positions and job applicants profiles is crucial for job seekers and
recruiters. The use of recruitment taxonomies has proven to be of
significant advantage in the area by enabling semantic matching and
reasoning. Hence, the development of Knowledge Bases (KB) where
curricula vitae and job offers can be uploaded and queried in order to
obtain the best matches by both, applicants and recruiters is highly
important. We introduce an approach to improve matching of profiles,
starting by expressing jobs and applicants profiles by filters
representing skills and competencies. Filters are used to calculate the
similarity between concepts in the subsumption hierarchy of a KB. This
is enhanced by adding weights and aggregates on filters. Moreover, we
present an approach to evaluate over-qualification and introduce blow-up
operators that transform certain role relations such that matching of
filters can be applied.