Top-k Retrieval for Automated Human Resource Management

نویسندگان

  • Umberto Straccia
  • Eufemia Tinelli
  • Tommaso Di Noia
  • Eugenio Di Sciascio
  • Simona Colucci
چکیده

We present a knowledge-based system, for skills and talent management, exploiting semantic technologies combined with top-k retrieval techniques. The system provides advanced distinguishing features, including the possibility to formulate queries by expressing both strict requirements and preferences in the requested profile and a semantic-based ranking of retrieved candidates. Based on the knowledge formalized within a domain ontology, the system implements an approach exploiting top-k based reasoning services to evaluate semantic similarity between the requested profile and retrieved ones. System performance is discussed through the presentation of experimental results.

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تاریخ انتشار 2009