An agent-based approach for building complex software systems
Communications of the ACM
IEEE Internet Computing
Trusting Information Sources One Citizen at a Time
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
A Computational Model of Trust and Reputation for E-businesses
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
Trust and Reputation Management in a Small-World Network
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Bayesian Network-Based Trust Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Trust Based Knowledge Outsourcing for Semantic Web Agents
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Reputation-Based Semantic Service Discovery
WETICE '04 Proceedings of the 13th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
A computational trust model for semantic web based on bayesian decision theory
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
Semantic similarity model for risk assessment in forming cloud computing SLAs
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems: Part II
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The Semantic Web will only achieve its full potential when users have trust in its operations and in the quality of services and information provided, so trust is inevitably a high-level and crucial issue. Modeling trust properly and exploring techniques for establishing computational trust is at the heart of the Semantic Web to realize its vision. We propose a scalable probabilistic approach to trust evaluation which combines a variety of sources of information and takes four types of costs (operational, opportunity, service charge and consultant fee) and utility into consider during the process of trust evaluation. Our approach gives trust a strict probabilistic interpretation which can assist users with making better decisions in choosing the appropriate service providers according to their preferences. A formal robust analysis has been made to examine the performance of our method.