What is it about?

What It's About Automatically converts natural language queries in Bengali into corresponding SQL queries to retrieve structured information from a database. Utilizes Named Entity Recognition (NER) to identify key entities (like names, locations, medical terms) in the user query. Bridges the gap between human-friendly language input and machine-understandable database queries, specifically in the Bengali language context. Why It Matters Enhances accessibility of database systems for Bengali-speaking users with no SQL knowledge. Promotes regional language NLP development and data democratization in healthcare, governance, or education.

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Why is it important?

1. Promotes Language Inclusivity It addresses a major gap by enabling Bengali language speakers—a large demographic—to interact with databases using natural language, promoting digital inclusivity in a field dominated by English-language systems. 2. Enhances Database Accessibility By converting natural language queries (NLQs) into structured SQL commands, it empowers users without technical or SQL knowledge to retrieve meaningful information from relational databases easily. 3. Named Entity Recognition (NER) Application The use of NER in Bengali—a morphologically rich and under-resourced language—is a technical milestone, helping the system identify important keywords, entities, and phrases for accurate query transformation. 4. Practical Use in E-Governance, Healthcare, Education Such a system can be applied in e-governance portals, healthcare records, student databases, and more, enabling efficient citizen-data interaction through native-language interfaces. 5. Foundation for Multilingual NLP Systems This work lays the groundwork for developing multilingual natural language interfaces for databases, an essential component in future AI-driven knowledge systems for developing countries.

Perspectives

Bridges Language Gap in Tech Access: Enables Bengali-speaking users to interact with databases using natural language, democratizing access to data-driven systems. NLP for Low-Resource Languages: Focuses on Bengali, a low-resource language in NLP, addressing a significant gap in multilingual language technologies. NER-Driven Query Transformation: Uses Named Entity Recognition (NER) to extract key components from queries, improving SQL generation accuracy and enabling precise information retrieval. Useful for Government, Education, and Healthcare Sectors: Facilitates easy access to structured data in domains like public health, student records, and administrative data without needing technical query skills. Foundation for Voice Assistants & Chatbots in Bengali: This system could be integrated into intelligent agents and chatbots that support voice/text queries in Bengali, expanding digital inclusion.

Dr. KAILASH PATI MANDAL
National Institute of Technology, Durgapur, West Bengal, India

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This page is a summary of: Natural Language Query in Bengali to SQL Generation Using Named Entity Recognition, December 2022, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/iatmsi56455.2022.10119243.
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