Today, there is a real challenge in accessing relevant information on the Web according to the user's needs and the context. There are always certain needs behind the user query and these queries are often ambiguous and shortened (especially in the case of mobile users), thus we need to handle the user queries intelligently to provide personalized results in a particular context. For improving user query processing, we present a context-based hybrid method for query expansion that automatically generates context-related terms. It considers the context as the actual state of the task that the user is undertaking when the information retrieval process takes place. The method uses the UML state diagram for modeling the current task and for detecting the transitions at time intervals with the task state changes. Furthermore, we introduce a new concept of SRQ (State Reformulated Queries), which is used to reformulate queries according to the user task context and the ontological user profile. Using experimental study, our approach has proved its relevance for certain contexts, the preliminary results are promising.