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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

25 Scopus Citations
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Author(s)

  • Xiangfeng Luo
  • Zheng Xuc
  • Qingliang Hu
  • Jie Yu
  • Xinhuai Tang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)2018-2032
Journal / PublicationConcurrency Computation Practice and Experience
Volume21
Issue number16
Publication statusPublished - Nov 2009

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.

Research Area(s)

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

Citation Format(s)

Generation of similarity knowledge flow for intelligent browsing based on semantic link networks. / Luo, Xiangfeng; Xuc, Zheng; Li, Qing; Hu, Qingliang; Yu, Jie; Tang, Xinhuai.

In: Concurrency Computation Practice and Experience, Vol. 21, No. 16, 11.2009, p. 2018-2032.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review