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Network based approach for discovering academic researchers with shared interests

  • Yunhong Xu*
  • , Zhihua Chang
  • , Jiejia Lin
  • , Jian Ma
  • , Jinxing Hao
  • , Dingtao Zhao
  • *Corresponding author for this work
  • University of Science and Technology of China
  • Southern University of Science and Technology
  • IRIS Systems (Shenzhen) Limit
  • City University of Hong Kong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Interest or expertise of researchers is a kind of knowledge in organization, like university, research institute and research community. Effectively management of this kind of knowledge can facilitate communication of members in organization and improve their performance. Researchers' cognitive limit and large amount of relevant information available present great challenge to discover researchers with similar interests. Previous research addresses this problem by investigating their research related relationships. The social network based approached do not satisfy researchers' needs due to their failure to take into consideration the semantic information related to research interests. To solve this problem, in this research, we propose an integrated model based on ontology and social network analysis to mine researchers with similar interests.

Original languageEnglish
Title of host publicationProceedings of the International Conference on E-Business and E-Government, ICEE 2010
Pages1864-1867
Number of pages4
DOIs
StatePublished - 2010
Externally publishedYes
Event1st International Conference on E-Business and E-Government, ICEE 2010 - Guangzhou, China
Duration: 7 May 20109 May 2010

Publication series

NameProceedings of the International Conference on E-Business and E-Government, ICEE 2010

Conference

Conference1st International Conference on E-Business and E-Government, ICEE 2010
Country/TerritoryChina
CityGuangzhou
Period7/05/109/05/10

Keywords

  • Expert finding
  • Knowledge management
  • Network analysis
  • Ontology
  • Similarity measure

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