PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Colucci S., Di Noia T., Ragone A., Ruta M., Straccia U., Tinelli E. Informative top-k retrieval for advanced skill management. Roberto De Virgilio, Fausto Giunchiglia, Letizia Tanca (eds.). Berlin: Springer Verlag, 2010.
 
 
Abstract
(English)
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.
URL: http://www.springerlink.com/content/978-3-642-04328-4#section=647860&page=1
DOI: 10/1007/978-3-642-04329-1
Subject Description Logics
Fuzzy
OWL 2
Skill Management
F.4 Mathematical Logic and Formal Language


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