A Technique of Analyzing Trust Relationships to Facilitate Scientific Service Discovery and Recommendation

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.

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