Mining Based Natural Language to Database Interface

Data/Information plays an important role in our daily life. This data can be generated from many sources like Hospitals, Organizations, and Educational Institutions etc. These data need to be managed and stored in a database. The database is the main source of information. To access, store and manipulate the data stored in a database is a critical task. This requires the knowledge of high level database languages like SQL, where the user writes the high level query to retrieve data from database. But this creates a complex problem for normal users, who are not aware of database languages. To minimize this complexity, NLQP (Natural Language Query System) is designed. This system provides an interface for the end users to write the query in natural language such as English and obtains the result back in Natural Language. The query written in natural language will be converted to SQL like queries by the NLQP system and the required results will be fetched from the database for the user. The main goal of NLQP system is to provide user friendly communication between the end user and the computer from where the data is to be fetched

A New Framework for Information System Development on Instant Messaging for Low Cost Solution

The increasingly inexpensive Internet has spurred the growth of online information system services in various companies. Almost all services are available in forms on web or mobile applications. For small companies, this particular system is more difficult to implement as it requires a substantial cost allocated for hosting, domain and server devices. The solution is to develop a framework for building information system services through Instant Messaging (IM) such as Telegram, Line or XMPP / Jabber using the Design Science Research Methodology. This proposed framework has the ability to transform the existing information system services into chat services with RBAC role, session, validation and natural interaction using Indonesian-language conversations. The framework that consists of Initiate layers, business process and communication, memory group and OLTP DBMS will produce low-cost solution for the development of integrated information systems service

Assemble geo-analytical questions through a Blockly-based natural language interface

Natural language Interfaces (NLIs) have the ability to make Geographic Information Systems more accessible for interdisciplinary researchers or any inexperienced users. However, the majority of research on NLIs for GIS explored NLIs for visualization or spatial data retrieval. Research on NLIs for geo-analytical questions is still lacking. Google Blockly, an open-source JavaScript library, is frequently used for developing visual programming editors for young students learning programming languages. Students set up program functions by selecting and assembling the programming blocks and the Blockly system automatically translates the block stacks into different programming languages. Similarly, we present a Blockly-based interface that generates a question depending on the blocks the user has assembled. It can be seen that a Blockly-based interface not only naturally represents syntactic structures in geo-analytical questions but also well assists users in familiarizing the blocks and generating clear and complete questions. A comprehensive usability study is still necessary to better evaluate the interface’s performance

Natural Language Interface to Relational Database (NLI-RDB) Through Object Relational Mapping (ORM)

The book is a timely report on advanced methods and applications of computational intelligence systems

Question Answering System Dengan Pendekatan Template Based Berbasis Linked Data Pada Data Kanker Prostat Pada Platform Telegram

Informasi kesehatan adalah satu dari beberapa informasi yang sering dicari di internet. Untuk mendapatkan informasi yang baik dan terpercaya sangat penting untuk memperhatikan kelengkapan dari informasi dan integrasi data. Semantic Web merupakan salah satu cara untuk menghubungkan data yang dibuat dalam suatu file RDF. Semantic Web yang telah dibangun untuk memberikan informasi mengenai kesehatan adalah BeinWell. BeinWell mengintegrasikan 5 dataset RDF agar pengguna dapat mengakses informasi secara luas. Di sisi lain, pengembangan dari Semantic Web BeinWell, sangat bergantung pada penggunaan SPARQL Query secara penuh agar pengguna mendapatkan informasi yang diinginkan dan penggunaan keyword untuk mencari informasinya. Oleh karena itu dibuatkan sebuah sistem yang mampu melakukan konversi terhadap bahasa yang digunakan manusia sehari-hari atau Natural Language menjadi SPARQL Query. Dengan sebuah Question Answering System ini, pengguna tidak perlu mengetikan bahasa query yang panjang, melainkan cukup hanya menggunakan bahasa inggris yang baik dan benar. Question Answering System ini dibangun dengan memanfaatkan pengembangan teknologi NLP, seperti POS Tagging, Wordnet dan algoritma Levenshtein Distance. Penggunaan NLP pada Question Answering System ini dimaksudkan untuk meningkatkan pemahaman semantik sistem terhadap sebuah pertanyaan, yang akan dimanfaatkan pada sebuah templat query yang dibangun. Berdasarkan hasil penelitian ini dihasilkan berupa Question Answering System natural language question ke SPARQL Query yang telah diimplementasikan dalam sebuah Chat bot pada platform Telegram. Sistem query yang dibangun dengan menggunakan kombinasi teknologi NLP ini mampu bekerja dengan ketepatan informasi yang diambil sebesar 88,88 persen dengan kecepatan proses mencapai 4,8 detik untuk merespon pertanyaan =============== Health information is one of the most frequently searched information on the internet. To get good and reliable information is very important to pay attention to the completeness of information and data integration. Semantic Web is one way to connect data created in an RDF file. A variety of semantic web to deliver health information that has been built, such as the BeinWell website. BeinWell integrates 5 RDF datasets for users to access information widely. On the other hand, the development of BeinWell's Semantic Web relies heavily on full SPARQL Query usage so that users get the desired information and keyword usage to find the information. Therefore created a system that is able to convert the language used everyday people or Natural Language into SPARQL Query. With a conversion system, users do not need to type a long query language, but simply use only good and correct English. This conversion system was built by utilizing the development of NLP technology, such as POS Tagging, Wordnet and Levenshtein Distance algorithms. The use of NLP in the conversion system is intended to increase the system's semantic understanding of a question, which will be exploited in a built-in query template. Based on the results of this study resulted in a natural language question conversion system to SPARQL Query that has been implemented in a Chat bot on the Telegram platform. The query system built using a combination of NLP technology is able to work with the accuracy of the information taken for 90 percen