Access to relevant information adapted to the needs and the context of the user is a real challenge in Web Search, owing to the increases of heterogeneous resources and the varied data on the web. There are always certain needs behind the user query, these queries are often ambiguous and shortened, and thus we need to handle these queries intelligently to satisfy the user's needs. For improving user query processing, we present a context-based hybrid method for query expansion that automatically generates new reformulated queries in order to guide the information retrieval system to provide context-based personalized results depending on the user profile and his/her context. Here, we consider the user context as the actual state of the task that the user is undertaking when the information retrieval process takes place. Thus State Reformulated Queries (SRQ) are generated according to the task states and the user profile which is constructed by considering related concepts from existing concepts in domain ontology. Using a task model, we will show that it is possible to determine the user's current task automatically. We present an experimental study in order to quantify the improvement provided by our system compared to the direct querying of a search engine without reformulation, or compared to the personalized reformulation based on a user profile only. The preliminary results have proved the relevance of our approach in certain contexts.