Extracting information from database is typically done by using a structured language such as SQL (Structured Query Language). But non expert users can't use this later. Wherefore using Natural Language (NL) for communicating with database can be a powerful tool. But without any help, computers can't understand this language; that is why it is essential to develop an interface able to translate user's query given in NL into an equivalent one in Database Query Language (DBQL). This paper presents a model of a generic natural language query interface for querying database. This model is based on machine learning approach which allows interface to automatically improve its knowledge base through experience. The advantage of this interface is that it functions independently of the database language, content and model. Experimentations are realized to study the performance of this interface and make necessary corrections for its amelioration
Nowadays several works have been proposed that allow users to perform fuzzy queries on relational databases. But most of these systems based on an additional software layer to translate a fuzzy query and a supplementary layer of a classic database management system (DBMS) to evaluate fuzzy predicates, which induces an important overhead. They are not also easy to implement by a non-expert user. Here we have proposed a simple and intelligent approach to extend the SQL language to allow us to write flexible conditions in our queries without the need for translation. The main idea is to use a view to manipulate the satisfaction degrees related to user-defined fuzzy predicates, instead of calculating them at runtime employing user functions embedded in the query. Consequently, the response time of executing a fuzzy query statement will be reduced. This approach allows us to easily integrate most fuzzy request characters such as fuzzy modifiers, fuzzy quantifiers, fuzzy joins, etc. Moreover, we present a user-friendly interface to make it easy to use fuzzy linguistic values in all clauses of a select statement. The main contribution of this paper is to accelerate the execution of fuzzy query statements.
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