Information on Web2.0, generated by users of web based services, is both difficult to organize and organic in nature. Content categorization and search in such situation offers challenging scenarios. The primary means of content categorization in such social services is folksonomy or collaborative tagging. During search in folksonomy, several issues arise due to lexical ambiguities in the way users choose tags to represent content. These are issues of different words representing the same concept, same words representing different concepts and variances in level of expertise of users. Past techniques to address these issues have worked on lexical analysis of term and have thus had only moderate levels of success. We have developed a model in which machine common sense and personalization is used to address these issues. In this paper, we explain our approach in detail, describe a prototype developed for the purpose of demonstrating feasibility of our approach and discuss an effectivene...