Study of Design Issues of Intelligent Databases

Abstract

We are living in the information age. Computer-based information technologies have been widely used to help many organizations, private companies, and academic and education institutions to manage their processes and information systems. Information systems are used to manage data. Databases are comprehensive element in private and public information systems which are gaining prime importance in a huge variety of application areas employing private and public information systems. newlineDatabase systems are the key to implementing data management. Data management requires database technique support. Using query language for dealing with databases is always a professional and complex problem. This complexity causes the user s usage of data existing in database limits to use definite reports there are in some pre implemented software. However, one can create this opportunity that each non computer professional provides its requirements in natural language to computer and retrieves the desired information by natural language processing. Natural language interfaces to databases (NLIDB) are systems that aim to bridge the gap between the languages used by humans and computers, and automatically translate natural language sentences to database queries. This thesis presents a method for building a Natural Languages Interfaces to Data Bases(NLIDB),named as Intelligent Query Converter (IQC) which can be used with any database. IQC translates Natural Language Queries into SQL through the use of domain dictionary which consists of Metadata set, semantic set of database and index files. Domain dictionary is created automatically which favors Domain Independence. newlineIQC System consists of two modules: pre-processor and runtime processor. The pre-processor reads the database schema and uses Wordnet to create the semantic sets and the set of production rules. It also reads the database records to create index files. The runtime processor identifies the keywords using the schema, the semantic sets, and pre-defined datasets.