Generation of similarity knowledge flow for intelligent browsing based on semantic link networks

X. Luo, Z. Xuc, Qing Li, Q. Hu, J. Yu, X. Tang

Research output: Journal article publicationConference articleAcademic researchpeer-review

25 Citations (Scopus)

Abstract

Similarity Knowledge Flow (SKF) is a kind of scientific workflow, providing an effective technique and theoretical support for intelligent browsing in the Web and e-Science environment. In this paper, a Semantic Link Networks (SLN)based SKF generation method is proposed. First, the topics are represented by Element Fuzzy Cognitive Maps then the semantic values of concepts/keywords and relations are calculated. Third, semantic similarity degrees between topics are calculated to build SLN-based semantic values of concepts and their relations in Element Fuzzy Cognitive Maps. In this way, similar relations at the keyword level are extended to the topic level. With the help of SLN and based on user's demand, SKF is generated as the browsing path of topics to guide user browsing behaviors. Finally, the semantic value of SKF is defined as a criterion to evaluate the browsing path of topics. Experimental results show that the browsing path of topics is easy to be activated by SKF which is generated by SLN. The proposed method has been proved to have a very good prospect in the fields of Web services and e-Science aplications. Copyright © 2009 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)2018-2032
Number of pages15
JournalConcurrency Computation Practice and Experience
Volume21
Issue number16
DOIs
Publication statusPublished - 1 Nov 2009
Externally publishedYes

Keywords

  • Element fuzzy cognitive map
  • Intelligent browsing
  • Semantic link network
  • Semantic web
  • Similar knowledge flow

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics

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