The goal of Web search personalization is to tailor search results to a particular user based on that user's interests and preferences, thus allowing for more efficient information access. One of the key factors for effective personalization of information access is the user context. We present an approach to personalized search that involves building models of users context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain the interest scores based on the user's ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile is effective in presenting the most relevant results to the user.