An Efficient Easily Adaptable System for Interpreting Natural Language Queries (original) (raw)

QUESTION ANSWERING SYSTEM WITH NATURAL LANGUAGE INTERFACE TO DATABASE

Question Answering (QA) is an area of natural language processing research aimed at providing human users with a convenient and natural interface for accessing information. Nowadays, the need to develop accurate systems gains more importance due to available structured knowledge-bases and the continuous demand to access information rapidly and efficiently. The need to store data in an organized manner so that searching, retrieving and maintaining of data becomes easier. To efficiently operate these database, knowledge of Structures Query Language (SQL)becomes essential. But the usage of SQL restricts the access to databases from the users who don't have the knowledge of them. A need for interface comes into the picture to enable the access of these databases even to a non-expert users. This paper describes the design to develop Telugu language Question Answering system to database. This paper describes about question answering system using Natural Language Interface to a database. Here we use the rule based algorithm for train the systems question classifier to achieve a high accuracy ratio. Keywords —Natural Language Processing (NLP), Natural Language Interface To Database (NLIDB), Question Answering System(QAS), Structured Query Language(SQL).

Masque/sql-- An Efficient and Portable Natural Language Query Interface for Relational Databases

1994

Masque is a powerful and portable natural language front-end for Prolog databases. It answers written English questions by generating Prolog queries, which a r e e v aluated against the Prolog database. This paper describes a modi ed version of Masque, called Masque/sql, w h i c h answers English questions by generating and executing SQL code. Masque/sql maintains the full linguistic coverage of the original Masque, and can be used with any database system supporting SQL. Masque/sql is e cient, and can be easily con gured using the built-in domaineditor.

AquaLog A Ontology-portable Question Answering interface for the Semantic Web

2005

As semantic markup becomes ubiquitous, it will become important to be able to ask queries and obtain answers, using natural language (NL) expressions, rather than the keyword-based retrieval mechanisms used by the current search engines. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input and returns answers drawn from the available semantic markup. We say that AquaLog is portable, because the configuration time required to customize the system for a particular ontology is negligible. AquaLog combines several powerful techniques in a novel way to make sense of NL queries and to map them to semantic markup. Moreover it also includes a learning component, which ensures that the performance of the system improves over time, in response to the particular community jargon used by the end users. In this paper we describe the current version of the system, in particular discussing its portability, its reasoning capabilities, and its learning mechanism.

Aqualog: An ontology-portable question answering system for the semantic web

2005

As semantic markup becomes ubiquitous, it will become important to be able to ask queries and obtain answers, using natural language (NL) expressions, rather than the keyword-based retrieval mechanisms used by the current search engines. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input and returns answers drawn from the available semantic markup.

Answering Complex Questions in Natural Language using Probabilistic Logic Programming and the Web

bnaic.gforge.uni.lu

We present an algorithm to answer complex questions in Natural Language that are a boolean combination of simple questions. The main feature of this algorithm is to use a probabilistic Prolog (ProbLog) to handle the uncertainty of answers obtained by information extraction systems such as TextRunner. The results shown in this paper indicate that the use of ProbLog improves the answers given by a system that parses complex questions with light linguistic mechanisms. Therefore, using a probabilistic setting can be seen as a promising approach for the enhancement of open information extraction systems based on weak linguistic algorithms.

Processing Natural Language Queries in Semantic Web Applications

Proceedings of the 3rd World Congress on Electrical Engineering and Computer Systems and Science, 2021

SPARQL is a powerful query language for an ever-growing number of Semantic Web applications. Using it, however, requires familiarity with the language which is not to be expected from the general web user. This drawback has led to the development of Question-Answering (QA) systems that enable users to express their information needs in natural language. This paper presents a novel dependency-based framework for translating natural language queries into SPARQL queries, which is based on the idea of syntactic parsing. The translation process involves the following five steps: extraction of the entities, extraction of the predicate, categorization of the query's type, resolution of lexical and semantic gaps between user query and domain ontology vocabularies; and construction of the SPARQL query. The proposed framework was tested on our closed-domain student advisory application intended to provide students with advice and recommendations about curriculum and scheduling matters. The advantage of our approach is that it requires neither any laborious feature engineering, nor complex model mapping of a query expressed in natural language to a SPARQL query template, and thus it can be easily adapted to a variety of domains.

Processing of natural language queries to a relational database

2003

A new method is developed to query a relational database in natural language (NL). Results: The method, based on a semantic approach, interprets grammatical and lexical units of a natural language into concepts of subject domain, which are given in a conceptual scheme. The conceptual scheme is mapped formally onto the logical scheme. We applied the method to query the FlyEx database in natural language. FlyEx contains information on the expression of segmentation genes in Drosophila melanogaster. The method allows formulation of queries in various natural languages simultaneously, and is adaptive to changes in the knowledge domain and user's views. It provides optimal transformation of queries from natural language to SQL, as well as visualization of information as a hyperscheme. The method does not require specification of all possible language constructions as well as a standard grammar accuracy in formulation of NL queries.