Enhancing the precision of content analysis in content adaptation using entropy-based fuzzy reasoning

Rick C.S. Chen, Stephen J.H. Yang, Jia Zhang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Content adaptation is a well-known technique to help portable devices present Web pages as smoothly as desktops do. Because of limited I/O and weak transmission capability, adaptations are usually performed by either transcoding or resizing multimedia components. In this paper, we propose a novel semantic coherence-retained content adaptation approach, namely functionality sense-based content adaptation (FSCA). Our goal is to avoid semantic distortions when rearranging a Web page on different screen sizes. Simulating entropy-based fuzzy reasoning in human cognition, we introduce Relevance of Functionality (RoF) to quantitatively represent the similarity intensity between two presentation objects (groups) based on their functionalities. We present an algorithm of calculating RoF and a procedure that uses RoF to decide content adaptation degree. Our experiments verify the feasibility and effectiveness of FSCA.

Original languageEnglish
Pages (from-to)5706-5719
Number of pages14
JournalExpert Systems with Applications
Volume37
Issue number8
DOIs
StatePublished - Aug 2010

Keywords

  • Content adaptation
  • Entropy
  • Fuzzy reasoning
  • Inductive reasoning
  • Longest common subsequence (LCS)
  • Semantic coherence

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