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A semantic-based social network of academic researchers
Abstract We proposed a framework to construct a semWe proposed a framework to construct a semantic-based social network of academic researchers to discover hidden social relationships among the researchers in a particular domain. The challenging task in in the process is to detect accurate relationships that exist among researchers according to their expertise and academic experience. In this paper, we first construct content-based profiles of researchers by crawling online resources. Then background knowledge derived from Wikipedia ,represented in a semantic kernel, is employed to enrich the researchers' profiles. Researchers' social network is then constructed based on the similarities among semantic-based profiles. Social communities are then detected by applying the social network analysis and using factors such as experience, background, knowledge level, personal preferences. Representative members of a community are identified using the eigenvector centrality measure. An interesting application of the constructed social network in academic conferences, when there is a need to assign papers to relevant researchers for the review process, is investigated.s for the review process, is investigated.
Abstractsub We proposed a framework to construct a semWe proposed a framework to construct a semantic-based social network of academic researchers to discover hidden social relationships among the researchers in a particular domain. The challenging task in in the process is to detect accurate relationships that exist among researchers according to their expertise and academic experience. In this paper, we first construct content-based profiles of researchers by crawling online resources. Then background knowledge derived from Wikipedia ,represented in a semantic kernel, is employed to enrich the researchers' profiles. Researchers' social network is then constructed based on the similarities among semantic-based profiles. Social communities are then detected by applying the social network analysis and using factors such as experience, background, knowledge level, personal preferences. Representative members of a community are identified using the eigenvector centrality measure. An interesting application of the constructed social network in academic conferences, when there is a need to assign papers to relevant researchers for the review process, is investigated.s for the review process, is investigated.
Bibtextype inproceedings  +
Doi 10.1007/978-3-642-31087-4_34  +
Has author Davoodi E. + , Kianmehr K. +
Has extra keyword Academic conferences + , Background knowledge + , Clustering analysis + , Content-based + , Eigenvector centralities + , Knowledge level + , Online resources + , Review process + , Semantic-based Similarity + , Social communities + , Social Network Analysis + , Social Networks + , Social relationships + , Wikipedia + , Education + , Industrial engineering + , Information retrieval + , Intelligent systems + , Semantics + , Social networking (online) + , Research +
Has keyword Clustering Analysis + , Information retrieval + , Semantic-based Similarity + , Social Network Analysis +
Isbn 9783642310867  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 323–332  +
Published in Lecture Notes in Computer Science +
Title A semantic-based social network of academic researchers +
Type conference paper  +
Volume 7345 LNAI  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 6 November 2014 16:36:37  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 16:36:37  +
DateThis property is a special property in this wiki. 2012  +
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