Trust and reputation management is introduced to the Online Social networks (OSNs) as a solution to promote a healthy collaboration relationship among participants. Currently, most trust and reputation systems focus on evaluating the credibility of the users. The reputation systems in OSNs have as objective to help users to make difference between trustworthy and untrustworthy, and encourage honest users by rewarding them with high trust values. Computing reputation of one user within a network requires knowledge of trust degrees between the users. In this paper, we develop a new Fuzzy Clustering Reputation algorithm, called FCR, based on trusted network. This algorithm computes the membership degrees for each user and classifies the users of OSNs by their trust similarity such that most trustworthy users belong to the same cluster. Moreover, to satisfy the needs of OSNs' users by storing important trust data in a well organized and easy to understand structure, we propose to enrich the Friend of Friend (FOAF) vocabulary with the reputation of users. Experimental results with data from the real OSN, Twitter, show that our work generates high quality results