Searching online text collections can be both rewarding and frustrating. On the same time valuable information can be found, typically many irrelevant documents are also retrieved and many relevant ones are missed. Word mismatches between the user's query and document contents are a main cause of retrieval failures. Expanding a user's query with related words can improve search performance, but finding and using related words is an open problem. On the basis of previous approaches to query expansion, this paper proposes a new approach to query expansion, which combines two popular traditional methods -thesauri and automatic relevance feedback. In terms of theoretical analysis and experiments, the new approach is effective to query expansion for Web retrieval and out-performs the optimized, conventional expansion approaches.