Many commercial websites, such as Target.com, which aspire to increase client’s transactions and thus profits, offer users easy-to-use pull-down menus and/or keyword searching tools to create advertisement (ads for short) queries that can be posted at their sites to extract the desired products. These websites, however, cannot handle natural language queries, which are formulated for specific information needs and can only be processed properly by natural language query processing (NLQP) systems. To solve the problem encountered by these commercial websites, we have developed a novel NLQP system, denoted AdProc, which retrieves database (DB for short) records that match information specified in ads queries on multiple ads domains. AdProc relies on an underlying database, which contains pre-processed (ads) records that provides the source of answers to users’ queries. AdProc automates the process of populating a DB using online ads and answering user queries on multiple ads domains. If there is no existing DB record that satisfies all the constraints specified in a user query, partially-matched DB records are extracted and ranked to meet the user’s information need, instead of retrieving no results. With the developed partial-match approach, AdProc allows users to examine partially-matched DB records, in addition to exact-matched records. The additional DB records are appealing to the end users, assuming that there is no exact-matched results or there are only a few retrieved results. Besides verifying the novelty of our exact-match approach, we have also included in the empirical study an evaluation to verify the merit of AdProc in retrieving partially-matched DB records. The performance evaluation shows that exact-matched records, as well as partially-matched records, are perceived as preferable by ordinary users based on real users’ assessments and the proposed natural language querying system outperforms a number of baseline ranking systems.