A NEW FEASIBLE NATURAL LANGUAGE DATABASE QUERY METHOD

Abstract

A metadata search approach is proposed to provide practical solutions to the natural language database query problem; where the metadata grow in largely linear manner and the search is linguistics-free. A new class of reference dictionary integrates four types of enterprise metadata: enterprise information models, database values, user-words, and query cases. The layered and scalable information models allow user-words to stay in original forms as users articulated them, as opposed to relying on permutations of individual words contained in the original query. A graphical representation method turns the dictionary into searchable graphs representing all possible interpretations of the input. A branch-and-bound algorithm then identifies optimal interpretations, which lead to SQL implementation of the original queries. Query cases enhance both the metadata and the search of metadata, as well as providing case-based reasoning to directly answer the queries. This design assures feasible solutions at the termination of the search, even when the search is incomplete (i.e., the results contain the correct answer to the original query). The necessary condition is that the text input contains at least one entry in the reference dictionary. The sufficient condition is that the text input contains a set of entries corresponding to a complete, correct single SQL query. Laboratory testing shows that the system obtained accurate results for most cases that satisfied only the necessary condition.