Data on the Web is noisy, huge and dynamic. This poses enormous challenges to most data mining techniques that try to extract patterns from this data. Web usage mining has recently attracted as viable framework for extracting useful access pattern information, such as user profiles, from massive amounts of web log data for the purpose of website personalization and organization. In this paper we present an approach for discovering and tracing evolving user profiles. We also describe how the discovered user profiles can be enriched with explicit information need that is inferred from search queries extracted from web log data.