• DocumentCode
    1825686
  • Title

    Identifying unreliable sources of skill and competency information

  • Author

    Fazel-Zarandi, Maryam ; Fox, Mark S.

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Toronto, Toronto, ON, Canada
  • fYear
    2013
  • fDate
    25-28 Aug. 2013
  • Firstpage
    1110
  • Lastpage
    1115
  • Abstract
    Organizations need to accurately understand the skills and competencies of their human resources in order to effectively respond to internal and external demands for expertise and make informed hiring decisions. In recent years, however, human resources have become highly mobile, making it more difficult for organizations to accurately learn their competencies. In such environment, organizations need to rely significantly on third parties to provide them with useful information about individuals. These sources and the information they provide, however, vary in degrees of trust and validity. In a previous paper, we developed an ontology for skills and competencies and modeled and analyzed the various sources of information used to derive the belief in an individual´s level of competency. In this paper, we present an approach based on social network analysis for identifying unreliable sources of competency information. We explore the conditions under which evaluations given by an individual or a group about another can be trusted. We evaluate this approach using recommendation data gathered by crawling user profiles in LinkedIn.
  • Keywords
    human resource management; information analysis; ontologies (artificial intelligence); social networking (online); LinkedIn; competency information; hiring decisions; human resources; information source; ontology; recommendation data; skill information; social network analysis; user profiles; Conferences; Educational institutions; Employment; LinkedIn; Organizations; Peer-to-peer computing; collusion detection; expert profiling; skill and competency management;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
  • Conference_Location
    Niagara Falls, ON
  • Type

    conf

  • Filename
    6785843