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.
|Titolo:||Informative top-k retrieval for advanced skill management|
|Titolo del libro:||Semantic Web Information Management - a model based perspective|
|Data di pubblicazione:||2010|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-642-04329-1_19|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|