Database Query Formation from Natural Language using Semantic Modeling and Statistical Keyword Meaning Disambiguation

This paper describes a natural language interface to database systems which is based on the query formation capabilities of a High-level Query Formulator. The formulator relies on the Semantic Graph of the database, which is a model of the data stored in the database. The natural language interface accepts a user input in natural language and extracts the necessary information needed by the formulator. This extraction process is performed using keywords obtained from the Semantic Graph and the database. Because keywords may have several meanings within a given domain, keyword meaning disambiguation is done using a statistical approach which involves comparing vectors of n-grams. N-grams are n contiguous words within a given text of natural language and they are capable of capturing lexical context. Traditionally , natural language interfaces have been heavy with grammars and other knowledge, but have been wide-ranging in functionality. The interface presented in this paper is more portable and exible in comparison, but sacriices some functionality. We feel such high-level interfaces to information systems will be needed as information systems become more readily available to more users.