Intelligent information systems must have the ability to seamlessly integrate knowledge from the short-term user activity, representing immediate users interests; long-term user profiles, representing established preferences; as well as knowledge from existing ontologies that provide an explicit representation of the domain of interest. Furthermore, such systems should be able to leverage a variety of sources of evidence to provide the best personalized experience for the user, including the semantic evidence associated with the user’s individual interaction. Our research focuses on an approach to personalized search that involves modeling the user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology.