The internet is a source of an increasingly enormous amount of information, and search engines assist in sifting through it to find the desired information. Personalized search engines have been developed which re-order results based on the user’s interests; however, current implementations appear to base their personalization algorithms on the URL of previously visited web pages, which do not fully capture the user’s interests. Previous research has successfully used hierarchical structures of concepts to better learn the user’s interests, and this research attempts to improve on these methods by using a flat-structured knowledge base in the form of Wikipedia to provide better personalized search features. Initial results show that the method improves on standard web search, but has yet to match the performance offered by previous methods. A number of problems and limitations have been outlined, correcting which may greatly improve the performance of this method.