IJMLC 2013 Vol.3(1): 17-20 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.264

Applying AHP Technique for Trust Evaluation in the Semantic Web

Bagher Rahimpour Cami and Amin Khodabandeh Amiri

Abstract—The increasing reliance on information gathered from the web and other internet technologies raises the issue of trust. Through the development of semantic Web,One major difficulty is that, by its very nature, the semantic web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each resource. We can’t expect eachuser to know the trustworthiness of each resource, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. Trust policiesand trust evaluation mechanisms are needed to filter untrustworthy resource. We tackle this problem by employing a trust model for evaluating trustworthiness ofeach resource. This proposed model uses semantic webmetadata, recommendation, and reputation as based factorfor evaluation algorithm. The weighting and Combination Methods are two main challenges for Proposed Trust evaluation algorithm. These factors have various type and semantic. Therefore we apply the AHP technique for trust evaluation that offers justification for trust decisions andcontrolled trust measurement.

Index Terms—AHP, content trust, hybrid trust model, semantic web, trust.

The authors are with Faculty of Computer & IT Engineering, Mazandaran University of Science & Technology, Babol, Iran (e-mail: rc_bagher@yahoo.com; aminkhodabandeh@gmail.com).

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Cite:Bagher Rahimpour Cami and Amin Khodabandeh Amiri, "Applying AHP Technique for Trust Evaluation in the Semantic Web," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 17-20, 2013.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net