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IJMLC 2017 Vol.7(5): 105-109 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2017.7.5.629

VSDR: Visualization of Semantic Data Representation for Information Search over Semantic Web

N. Kanjanakuha, C. Techawut, R. Sukhahuta, and P. Janecek

Abstract—Many semantic searching tools for encyclopedia searching are presently available on the Internet. Most of them display results based on some textual representations. Some visual representations are rarely found by having visual interfaces mirroring link data structure from any sources. However, they hardly interact with a large number of searching results on a screen because of some complicated filtering tools and results unrelated to user requirements. This paper presents Visualization of Semantic Data Representation (VSDR) as a framework for displaying a 3-layer approach based on the hyperbolic tree model. It helps users to mainly focus on a linked data structure that hides some complex query scenarios from users occurring while searching through a semantic web. It can also leverage some difficulties of user’s exploration, perception, and interpretation with direct manipulation. The prototype of VSDR is constructed and evaluated by means of user experiment in order to assess overall usability of the proposed 3-layer approach based on the hyperbolic tree model, and the relevance between results and user requirements.

Index Terms—Knowledge exploration, visual exploration, human–computer interaction, semantic search.

N. Kanjanakuha, C. Techawut, R. Sukhahuta are with the Department of Computer Science, Chiang Mai University, Chiang Mai, 50200 Thailand (e-mail: natanun.ka@cmu.ac.th, churee.t@cmu.ac.th, rattasit.s@cmu.ac.th).
P. Janecek is with the Think Blue Data Co., Ltd., Nonthaburi, 11120, Thailand (e-mail: paul.janecek@gmail.com).

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Cite: N. Kanjanakuha, C. Techawut, R. Sukhahuta, and P. Janecek, "VSDR: Visualization of Semantic Data Representation for Information Search over Semantic Web," International Journal of Machine Learning and Computing vol. 7, no. 5, pp. 105-109, 2017.

General Information

  • ISSN: 
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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