Published in

Open Science Framework, 2017

DOI: 10.17605/osf.io/7y2fv

Springer Verlag, Lecture Notes in Computer Science, p. 295-302

DOI: 10.1007/978-3-319-44406-2_23

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Top-k Matching Queries for Filter-Based Profile Matching in Knowledge Bases

Journal article published in 2016 by Alejandra Lorena Paoletti, Jorge Martinez-Gil ORCID, Klaus-Dieter Schewe
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Preprint: policy unknown
Question mark in circle
Postprint: policy unknown
Question mark in circle
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Abstract

Finding the best matching job offers for a candidate profile or, the best candidates profiles for a particular job offer, respectively constitutes the most common and most relevant type of queries in the Human Resources (HR) sector. This technically requires to investigate top-k queries on top of knowledge bases and relational databases. We propose in this paper a top-k query algorithm on relational databases able to produce effective and efficient results. The approach is to consider the partial order of matching relations between jobs and candidates profiles together with an efficient design of the data involved. In particular, the focus on a single relation, the matching relation, is crucial to achieve the expectations.