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The Semantic Web, as a decentralized complex system, is akin to be fuzzy and evolutionary. In this article, a comprehensive survey on the applications of variant Computational Intelligence methods to enhance a variety of Semantic Web applications is provided. The survey consists of three aspects: fuzzy logic to deal with vagueness and uncertainty in Web semantics; evolutionary computations to deal with the vastness and tractability issues in storing, querying, reasoning and mapping semantic data; artificial neural network to improve the learning capability of the Semantic Web. Based on the survey of the existing approaches in the literature, some potential future research directions in this area have also been discussed and proposed.