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
Link To Document :
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