An intelligent system for information extraction from relational database using HMM

Abstract

In the present computing and fast growing world, information plays a key role in humans day to day life. Information can be stored in databases in an organized manner and SQL allows users to access process and manage data on database. But not all users are accustomed to writing SQL queries. This resulted in need for non-sophisticated users to access the database using natural language. A Natural Language Interface for Database (NLIDB) acts as an intelligent interface for database which allows users to access the database by typing requests in their natural language. The paper is going to discuss about a NLIDB system based on HMM. The system contains two modules: linguistic module and database module. Linguistic module makes uses of various Natural Language Processing (NLP) techniques which are used to identify tokens that correspond to the constraints and predicates required to build possible SELECT, WHERE and FROM clauses. The output of linguistic module is passed to database module. Identified tokens are then mapped to SQL query tokens using a query translation algorithm. A template is being used to generate corresponding SQL query. If the algorithm fails to identify sufficient details, a query prediction module based on HMM identifies the attributes and predicates list. The system used either a template based system that produce nearly 100% correct result to user query or prediction module that generate a highest probable result that matches to the user query. No situation exists in which the system fails to produce a result to the NL query.