SURVEY OF NATURAL LANGUAGE INTERFACE TO DATABASES (original) (raw)

A Comprehensive Study on Natural Language Processing and Natural Language Interface to Databases

It was highly desirable for a machine to interact more friendly with the users so that the field of Natural Language Processing (NLP) emerged and Natural Language Interface to Databases (NLIDBs) systems are built and design. A major problem faced by the users of the data bases is that the databases generally make use of special purpose languages familiar only to the trained users like Structured Query Language (SQL). Natural Language Interface to Databases provides the interface in which queries are written in the form Natural Language. These queries are passed through the machine, machine translates these queries. There are different levels of it, after passing these levels machine produce relevant results. This paper will provides comprehensive understanding about Natural Language Processing and Natural Language Interface to Databases.

Natural Language Interfaces to Databases: An Analysis of the State of the Art

People constantly make decisions based on information, most of which is stored in databases. Accessing this information requires the use of query languages to databases such as SQL. In order to avoid the difficulty of using these languages for users who are not computing experts, Natural Language Interfaces for Databases (NLIDB) have been developed, which permit to query databases through queries formulated in natural language. Although since the 60s many NLIDBs have been developed, their performance has not been satisfactory, there still remain very difficult problems that have not been solved by NLIDB technology, and there does not yet exist a standardized method of evaluation that permits to compare the performance of different NLIDBs. This chapter presents an analysis of NLIDBs, which includes their classification, techniques, advantages, disadvantages, and a proposal for a proper evaluation of them.

Natural Language Interface for Web-based Databases

Advances in the work on interfaces that facilitate access to databases through Internet are presented. Increasing needs of the users that access computer resources, the technological advance in this field, and the limitations of the graphic interfaces and forms motivate the development of new solutions in human-machine interfaces. In recent years, natural language processing has received a new impulse and achieved sufficient maturity to become a real solution in human-machine interfaces. A general architecture of a system of natural language interface to Web-based databases is described, as well as the current advance of the project. A detailed review of the history and the state of the art of the problem is given.

An Intelligent Database System using Natural Language Processing

Enormous amount of data are being processed and exchanged in our daily life, and database, which is used to organize data has been an active research topic for a long time. Database plays a major role in many computer systems and there is always a demand from technical and nontechnical people to ease the process of accessing data on database. Using Natural Language to directly interact with a database is a nice and user friendly solution. In order to achieve this type of communication between the computer (In particular, database) and human we have to make the computer understand what the human asks, and then, be able to respond with the right answer that was expected to be extracted from the database. In this paper we present an intelligent system for converting Natural Language queries into equivalent database Structured Query Language (SQL). Our system also allows processing complex Natural Language queries. We call this Intelligent Agent based Natural Language Interface to Database (INLIDB). The query results from the INLIDB is presented in an attractive succinctly viewable format. We have obtained encouraging results from INLIDB.

The history and recent advances of Natural Language Interfaces for Databases Querying

2021

Databases have been always the most important topic in the study of information systems, and an indispensable tool in all information management systems. However, the extraction of information stored in these databases is generally carried out using queries expressed in a computer language, such as SQL (Structured Query Language). This generally has the effect of limiting the number of potential users, in particular non-expert database users who must know the database structure to write such requests. One solution to this problem is to use Natural Language Interface (NLI), to communicate with the database, which is the easiest way to get information. So, the appearance of Natural Language Interfaces for Databases (NLIDB) is becoming a real need and an ambitious goal to translate the user’s query given in Natural Language (NL) into the corresponding one in Database Query Language (DBQL). This article provides an overview of the state of the art of Natural Language Interfaces as well ...

Generic Interactive Natural Language Interface to Databases (GINLIDB)

To override the complexity of SQL, and to facilitate the manipulation of data in databases for common people (not SQL professionals), many researches have turned out to use natural language instead of SQL. The idea of using natural language instead of SQL has prompted the development of new type of processing method called Natural Language Interface to Database systems (NLIDB). The NLIDB system is actually a branch of more comprehensive method called Natural Language Processing (NLP). In general, the main objective of NLP research is to create an easy and friendly environment to interact with computers in the sense that computer usage does not require any programming language skills to access the data; only natural language (i.e. English) is required.

An Advanced Natural Language Interface to Databases

Database management systems have been widely used for storing and retrieving data. However, databases are difficult to use and there interface is complex and the same is difficult to access. To make it easy for a user to retrieve data, an interface is developed in which a database can be accessed by a user through querying in Hindi language and to get the result in same language. In order to develop an improved Hindi language graphical user interface to database management system. The proposed system can handle single and multiple columns retrieval queries, selection of whole table, conditional queries (between, in), join queries and queries that include nested, functions and logical operators. Since a user should not be able to update or delete data from database so the user is suggest on selection queries.

Natural Language Interfaces to Databases: A Survey on Recent Advances

Handbook of Research on Natural Language Processing and Smart Service Systems, 2021

This chapter consists of an update of a previous publication. Specifically, the chapter aims at describing the most decisive advances in NLIDBs of this decade. Unlike many surveys on NLIDBs, for this chapter, the NLIDBs will be selected according to three relevant criteria: performance (i.e., percentage of correctly answered queries), soundness of the experimental evaluation, and the number of citations. To this end, the chapter will also include a brief review of the most widely used performance measures and query corpora for testing NLIDBs.

Features and Pitfalls that Users Should Seek in Natural Language Interfaces to Databases

Studies in Computational Intelligence, 2014

Natural Language Interfaces to Databases (NLIDBs) are tools that can be useful in making decisions, allowing different types of users to get information they need using natural language communication. Despite their important features and that for more than 50 years NLIDBs have been developed, their acceptance by end users is very low due to extremely complex problems inherent to natural language, their customization and internal operation, which has produced poor performance regarding queries correctly translated. This chapter presents a study on the main desirable features that NLIDBs should have as well as their pitfalls, describing some study cases that occur in some interfaces to illustrate the flaws of their approach.