Informative Top-k Retrieval for Advanced Skill Management

The paper presents a knowledge-based framework for skills and talent management based on an advanced matchmaking between profiles of candidates and available job positions. Interestingly, informative content of top-k retrieval is enriched through semantic capabilities. The proposed approach allows to: (1) express a requested profile in terms of both hard constraints and soft ones; (2) provide a ranking function based also on qualitative attributes of a profile; (3) explain the resulting outcomes (given a job request, a motivation for the obtained score of each selected profile is provided). Top-k retrieval allows to select most promising candidates according to an ontology formalizing the domain knowledge. Such a knowledge is further exploited to provide a semantic-based explanation of missing or conflicting features in retrieved profiles. They also indicate additional profile characteristics emerging by the retrieval procedure for a further request refinement. A concrete case study followed by an exhaustive experimental campaign is reported to prove the approach effectiveness. Simona Colucci Politecnico di Bari, via E. Orabona 4, I-70125 Bari,Italy, e-mail: [email protected] Data Over Ontological Models D.O.O.M. srl, via N. Paganini 7, I-75100 Matera, Italy, e-mail: [email protected] Tommaso Di Noia Politecnico di Bari, via E. Orabona 4, I-70125 Bari, Italy, e-mail: [email protected] Azzurra Ragone Politecnico di Bari, via E. Orabona 4, I-70125 Bari, Italy, e-mail: [email protected] Michele Ruta Politecnico di Bari, via E. Orabona 4, I-70125 Bari, Italy, e-mail: [email protected] Umberto Straccia ISTI-CNR, Via G. Moruzzi 1, I-56124 Pisa, Italy, e-mail: [email protected] Eufemia Tinelli (corresponding author) Politecnico di Bari, via Orabona 4, I-70125 Bari, Italy, e-mail: [email protected]