Users who are unfamiliar with database query languages can search XML data sets using keyword queries. Previous work has shown that current XML keyword search methods, although intuitive, do not effectively use the data's structural information and provide poor precision, recall, and ranking for most queries. Based on an extension of the concept of information theory, we have developed principled frameworks called normalized total correlation (NTC) and normalized term presence correlation (NTPC) to measure the relevance of candidate answers to keyword queries. We demonstrate EXTRUCT, an XML keyword search interface that uses NTC and NTPC. An extensive empirical evaluation over two real-world XML DBs has shown that EX-TRUCT has better precision and recall and provides better ranking than all previous approaches. We demonstrate EXTRUCT, along with seven other keyword search systems for four real-world XML data sets, using prepared queries as well as queries from the audience. The demonstration shows that using deep structural information increases the effectiveness of XML keyword search systems considerably.