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Most of the existing service discovery methods focus on finding candidate services based on functional and non-functional requirements. However, while the open science community engenders many similar scientific services, how to differentiate them remains a challenge. This paper proposes a trust model that leverages the implicit human factor to help quantify the trustworthiness of candidate services. A hierarchical Knowledge-Social-Trust (KST) network model is established to draw hidden information from various publication repositories (e.g., DBLP) and social networks (e.g., Twitter). As a proof of concept, a prototyping service has been developed to help scientists evaluate and visualize trust of services. The performance factor is studied and experience is reported.
Twitter, Software, Measurement, Social factors, XML

J. Zhang et al., "A Technique of Analyzing Trust Relationships to Facilitate Scientific Service Discovery and Recommendation," 2013 IEEE International Conference on Services Computing(SCC), Santa Clara, CA, USA USA, 2013, pp. 57-64.
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