During the last several years, the volume of user-generated content on the web has skyrocketed. Today, the major Internet players such as MySpace, Wordpress, and YouTube all provide a variety of Web 2.0 based applications that allow users to post photos, share video, and manage blogs with multimedia content. Keyword-based tags are used to classify the submitted content and to conduct searches for retrieval. Use of simple keyword-based tags provide less than optimal classification leading to poor search results. This work examines several proposed solutions to this problem and proposes a solution based on semantic tagging of online contents. These proposed semantic tags are intended to be ?understandable? to both machines and humans; additionally we allow the user to specify a context for each tag. The combination of these measures permits searches based on inferences and synonyms in addition to simple keyword matches. Initial tests demonstrate that use of the proposed scheme leads to more accurate content classification and search results with a greater number of hits and higher relevancy. We describe the design and, implementation of a prototype system as proof of concept of our proposed solution. The prototype has been implemented with extensive use of open source software such as Jena, jBoss, the Spring framework, PostgreSQL database, and other freely available resources such as Wordnet.