Providing Inferential Capability to Natural Language Database Interface

Not everybody is able to write SQL (Structured Query Language) queries as they may not be aware of the structure of the database. So there is a need for non-expert users to query relational databases in their natural language. The idea of using natural language instead of SQL, has promoted the development of Natural Language Interface to Database systems (NLIDB). The traditional Information Retrieval Models were based on approximation and lexical mapping which had its own deficiencies. System is inadequate if the query uses hypernyms (broad category words). If a user is using synonyms of lexicon then the system is unable to access the database. Homonymous keyword in query may arise ambiguity and possibly produce erroneous result because the system is unable to distinguish actual meaning of homonyms. In a query, lexemes may be related to each other and produce a collated meaning which is not considered by the classical system. To overcome these limitations, a knowledgebase can be provided to the NLIDB. The knowledgebase will provide inferential capability to the systems using a collection of hypernyms, synonyms, homonyms, discourse and other information required to produce accurate results.