An Arabic natural language interface for querying relational databases based on natural language processing and graph theory methods

2018 ◽  
Vol 10 (2) ◽  
pp. 155 ◽  
Author(s):  
Hanane Bais ◽  
Mustapha Machkour ◽  
Lahcen Koutti
Keyword(s):  
Graph Theory ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Relational Databases ◽  
Natural Language Interface

scholarly journals Hash Tables for a Digital Lexicon

2021 ◽  
Vol 28 (2) ◽  
pp. 25-38
Author(s):  
Fábio Carlos Moreno ◽  
Cinthyan Sachs C. de Barbosa ◽  
Edio Roberto Manfio
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Data Structures ◽  
Response Speed ◽  
Hash Tables ◽  
Processing Data ◽  
Natural Language Interface ◽  
Performance Results ◽  
And Storage

This paper deals with the construction of digital lexicons within the scope of Natural Language Processing. Data Structures called Hash Tables have demonstrated to generate good results for Natural Language Interface for Databases and have data dispersion, response speed and programming simplicity as main features. The storage of the desired information is done by associating a key through the hashing functions that is responsible for distributing the information in this table. The objective of this paper is to present the tool called Visual TaHs that uses a sparse table to a real lexicon (Lexicon of Herbs), improving performance results of several implemented hash functions. Such structure has achieved satisfactory results in terms of speed and storage when compared to conventional databases and can work in various media, such as desktop, Web and mobile.

scholarly journals SpeakQL Natural Language to SQL

2021 ◽  
Vol 40 ◽  
pp. 03018
Author(s):  
Dhairya Shah ◽  
Aniruddha Das ◽  
Aniket Shahane ◽  
Dharmik Parikh ◽  
Pranit Bari
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Relational Databases ◽  
Query Language ◽  
Automatic Generation ◽  
Query Languages ◽  
Natural Language Interface ◽  
Open Issue ◽  
Conventional Structure ◽  
Structure Query

Incorporating SQL questions from normal language is a long-standing open issue and has been drawing in extensive intrigue as of late. Natural Language Interface (NLI) is the confluence of Natural Language Processing (NLP) and Human-Computer Interaction, which allows interaction between humans and computers through the utilization of Natural Language. Here we are gonna deal with the problem of automatic generation of Structured Query Language (SQL) queries. SQL is a database language for querying and manipulating relational databases. Despite the spectacular rise in the acceptance of relational databases, there is a fundamental limitaion to the ability to fetch data from those databases. One of the major reasons for this is the fact that the users of these relational databases need to comprehend convoluted structured query languages. In this body of work, we present an interface that allows users to interact with the databases using Natural Lanaguage as opposted to the conventional structure query languages.

scholarly journals A qualitative analysis framework using natural language processing and graph theory

2012 ◽  
Vol 13 (5) ◽  
pp. 173 ◽  
Author(s):  
Patrick Tierney
Keyword(s):  
Graph Theory ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Qualitative Data ◽  
Analysis Framework ◽  
Data Set ◽  
Limited Experience ◽  
Time Required ◽  
Positivist Approach

<p style="margin-bottom: 0in; line-height: 200%;">This paper introduces a method of extending natural language-based processing of qualitative data analysis with the use of a very quantitative tool—graph theory. It is not an attempt to convert qualitative research to a positivist approach with a mathematical black box, nor is it a “graphical solution”. Rather, it is a method to help qualitative researchers, especially those with limited experience, to discover and tease out what lies within the data. A quick review of coding is followed by basic explanations of natural language processing, artificial intelligence, and graph theory to help with understanding the method. The process described herein is limited by neither the size of the data set nor the domain in which it is applied. It has the potential to substantially reduce the amount of time required to analyze qualitative data and to assist in the discovery of themes that might not have otherwise been detected.<br /><br /></p>

scholarly journals Pemanfaatan Bahasa Alami Dalam Penelusuran Informasi Skripsi Melalui Digital Library

2020 ◽  
Vol 2 (1) ◽  
pp. 22-31
Author(s):  
Dewi Soyusiawaty ◽  
Anna Hendri Soleliza Jones
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Digital Library ◽  
Information Search ◽  
Context Free Grammar ◽  
Flexible System ◽  
Search Form ◽  
Natural Language Interface ◽  
Context Free

Daftar judul skripsi yang ada di web digilib.uad.ac.id belum digunakan secara optimal. Kesulitan menentukan topik serta pengelolaan data pada sistem yang belum memadai menjadi beberapa kendala mahasiswa. Penelitian ini bertujuan membangun pencarian informasi skripsi dengan antarmuka bahasa alami agar mudah dalam menulis kriteria pencarian tanpa harus terikat formulir pencarian. Penelitian ini menggunakan data skripsi untuk dikelola. Pengambilan data dilakukan menggunakan pendekatan Natural Language Processing. Masukan dalam bentuk kalimat bahasa alami digunakan untuk mencari data.  Proses Parsing dilakukan untuk memecah kalimat input dan mendeteksi kata kunci yang relevan. Pengembangan aturan produksi dalam Context Free Grammar diperlukan untuk menerjemahkan bahasa alami ke dalam query.  Kalimat yang melewati tahap parser diterjemahkan ke dalam bahasa SQL. Sistem ini berhasil menampilkan informasi skripsi berupa daftar judul berdasarkan topik, metode, dan objek penelitian sesuai kalimat pencarian dengan nilai precision sebesar 89,3% dan recall sebesar 100%. Keberadaan model pencarian informasi dengan antarmuka bahasa alami dapat menjadi alternatif dalam proses pencarian informasi skripsi guna menyediakan sistem yang lebih fleksibel.  The use of the existing digital library has not been used optimally. Difficulties in determining topics and managing data in an inadequate system are among the obstacles for students. This study aims to build a thesis information search with a natural language interface so that it is easy to write search criteria without having to be tied to a search form. This research uses thesis data to be managed. Data were collected using the Natural Language Processing approach. Input in the form of natural language sentences is used to find data. The parsing process is carried out to break down the input sentence and detect relevant keywords. Development of production rules in Context Free Grammar is necessary to translate natural language into queries. Sentences that go through the parser stage are translated into SQL language. This system succeeds in displaying thesis information in the form of a list of titles based on topics, methods, and research objects according to the search sentence with a precision value of 89% and a recall of 100%. The existence of an information retrieval model with a natural language interface can be an alternative in the thesis information search process in order to provide a more flexible system.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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