 Natural-language front ends for databases have long been considered an attractive application of NLP technology, beginning with such systems as LUNAR (Woods et al.  1972) and REL (Thompson et al.  1969; Thompson &amp; Thompson 1975). The attractiveness lies in the fact that retrieval and manipulation of information from a relational (or other uniformly structured) database can be assumed to be handled by an existing db query language and process. This feature sharply limits the kinds of natural language questions to be expected from a user, such as questions aimed at retrieving objects or tuples of objects satisfying given relational constraints, or providing summary or extremal properties (longest rivers, lowest costs, and the like) about them. It also greatly simplifies the interpretive process and question-answering, since the target logical forms—formal db queries—have a known, precise syntax and are executed automatically by the db management system, leaving only the work of displaying the computed results in some appropriate linguistic, tabular or graphical form.