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Traditional web search engines are keyword-based. Such a mechanism is effective when the user knows exactly the right words in the web pages they are looking for. However, it doesn’t produce good results if the user asks for a concept or topic that has broader and sometimes ambiguous meanings. In this paper, we present a framework that improves web search experiences through the use of a probabilistic knowledge base. The framework classifies web queries into different patterns according to the concepts and entities in addition to keywords contained in these queries. Then it produces answers by interpreting the queries with the help of the knowledge base. Our preliminary results showed that the new framework is capable of answering various types of concept-based queries with much higher user satisfaction, and is therefore a valuable addition to the traditional web search.