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Crowdsourcing provides a valuable means for accomplishing large amounts of work which may require a high level of expertise. We present an algorithm for computing the trustworthiness of user-contributed tags of artifacts, based on the reputation of the user, represented as a probability distribution, and on provenance of the tag. The algorithm only requires a small number of manually assessed tags, and computes two trust values for each tag, based on reputation and provenance. We moreover present a computationally cheaper adaptation of the algorithm, which clusters semantically similar tags in the training set, and builds an opinion on a new tag based on its semantic relatedness with respect to the medoids of the clusters. Also, we introduce an adaptation of the algorithm based on the use of provenance stereotypes as an alternative basis for the estimation. Two case …