iTANONG: A Natural Language Interface to Databases for Filipinos

23 May 2022 7:40 AM

The iTANONG project is an AI system that would allow the user to access information stored in a database and generate insights from this information just by typing requests or questions expressed in Filipino, English, or Taglish.



iTANONG is a 3-year project proposal which starts in 2022 with an aim to develop a natural language interface to relational databases (NLIDB). This artificial intelligence (AI) application can be accessed through a web browser or a mobile app that will enable end-users to key in questions in Filipino, English, or TagLish (more languages can be accommodated for later research). The answer can be derived from the connected relational database management systems (RDBMS). This linguistic interface will precisely fill the gap between the users and the RDBMS being maintained by an organization.


January 1, 2022 – December 31, 2024 




The general objective is to make important databases more accessible to the end-users, especially Filipinos who can hardly express themselves well in English much less in technical or computer language. Specifically, the researchers would like to achieve the following objectives:  

  1. To build a natural language querying engine (NLQE) that can interface with any database or flat-file tables to elicit information and insights for business value and actionable decisions.  
  2. To carry out and enable state-of-the-art research in machine learning, deep learning, data science, natural language processing, and artificial intelligence and consequently institute research laboratories in these areas.  
  3. To conduct capacity building for researchers and technology adopters in deep learning, software engineering, and natural language processing.  
  4. To push the widespread adoption of the technology generated from this research, starting from the DOST-ASTI and consequently in the whole DOST and other government agencies. 
  5. To commercialize the products and technologies that will be generated from the research. 


Potential Industry Partners/Adopters

Artificial Intelligence and Data Science are deemed to be emerging technologies that industries need to leverage and are critical to national development. This project will bank on existing capacities and focus on value-addition, efficient processes, clarity of information, fast delivery, and reliable service. Through research and development (R&D), this project will support human resource and institution development, information and technology diffusion, and the development of enabling policies. In effect, it would help the Department of Trade and Industry’s strategy of promoting industry competitiveness to sustain the tremendous growth in the business and industry sectors through data accessibility, operational efficiency, and actionable insights as well as develop business opportunities in frontier sectors using Artificial Intelligence. Such goals are anchored in the Government’s long-term goal of attaining genuine and inclusive growth. This project was formulated in consultation with the private sector (technology-to-market pull), so it would be very marketable if pushed through. The benefits of this project transcend beyond making information transparent and accessible but will also make industries more competitive to lead the country back to economic recovery. Below are some entities that will surely benefit from the system: 

Government Offices: 

  •     NGAs (will facilitate government data integrations and interoperability) 
  •     DOST agencies 
  •     FOI receiving officers 
  •     SSS 
  •     GSIS 
  •     PSA 
  •     Pag-IBIG Fund 
  •     BIR (for efficient tax collection) 
  •     DSWD (for fast accurate tracking of social protection programs like ‘ayuda’ and conditional cash transfers) 

Private Business Organizations: 

  •    Banks 
  •    Retail 
  •    Airline Companies (travelers can easily inquire about their flight bookings.) 
  •  E-Commerce Apps (like LAZADA/Shopee for customer inquiries) 

Academic Institutions: 

  • Students can inquire about their enrolment status. 
  • Learning modules can be organized in databases for easy access. 

Disaster Management:  

  • Fast and easy access to valuable information for pre- and post-disaster assessment 
  • Researchers and scientists can potentially access disparate scientific data related to weather, storms, and earthquakes. 



Staff Involved

Project Leaders

Elmer C. Peramo - Project Leader ​/ Technical Lead

Joanna G. Syjuco - Division Chief / Project Management

Jeffrey A. Aborot - Senior Technical Consultant​​

Roxanne S. Aviñante - Senior Technical Consultant​​

Michelle P. Neverida - Senior Business Systems Analyst ​​/ Project Co-Technical Lead

Vanesa O. Osiana - Senior Project Manager

John Kevin C. Abonita - Senior Software Quality Assurance

Nia Bernise F. Fabay  - Lead Software Developer

Eduardo D. Piedad Jr. - S&T Fellow I

Elisa C. Espares - Project Division Assistant

Aunhel John M. Adoptante - Science Research Specialist I

Mark Jerome T. Tulali - Science Research Specialist I


Project Staff

Alexandra C. Solidum  - Project Manager

Christopher S. Belaos  - Full Stack Software Developer

Mark Kevin P. Carumba  - Full Stack Software Developer

Johannah Claire G. Remonte - Web App Tester​

Annjaneth F. Diabordo - Business Analyst/UI/UX 

Christalline Joie B. Llarena  - NLP Engineer

Moses L. Visperas  - Outsourced NLP Engineer (Sep 2022 to Jan 2024)

Ma. Flores R. Tuscano - Outsourced Web App Tester (Mar 2022 to Sep 2023)

Ma. Teresita A. Abia  - Outsourced Project Manager (Jan 2022 to May 2023)

Christopher Nabo - Outsourced Web App Tester (Mar 2022 to Apr 2023)

Regine Allaine L. Reyes - Outsourced Business Analyst/UI/UX (Jan 2022 to Feb 2023)