Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation

📰 ArXiv cs.AI

Learn how to enable natural language queries for NoSQL databases using text-to-NoSQL translation, bridging the gap between user intent and database queries

advanced Published 16 Jun 2026

Action Steps

Implement a text-to-NoSQL translation system using MongoDB aggregation pipelines
Use natural language processing techniques to parse user queries and identify entities, nested paths, and arrays
Configure the system to handle missing fields and dynamic keys in the NoSQL database
Test the system with various natural language queries to ensure accuracy and efficiency
Apply the text-to-NoSQL translation technique to other NoSQL databases, such as Cassandra or Couchbase

Who Needs to Know This

Data scientists, software engineers, and database administrators can benefit from this technique to improve user experience and simplify query generation for NoSQL databases

Key Insight

💡 Text-to-NoSQL translation can simplify query generation for NoSQL databases and improve user experience

Key Takeaways

Learn how to enable natural language queries for NoSQL databases using text-to-NoSQL translation, bridging the gap between user intent and database queries

Full Article

Title: Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation

Abstract:
arXiv:2502.11201v3 Announce Type: replace-cross Abstract: NoSQL databases are core data infrastructure, yet natural-language access to them remains underdeveloped: correct query generation must recover how a non-relational data model represents entities, nested paths, arrays, missing fields, and dynamic keys. This paper studies Text-to-NoSQL, translating natural-language requests into executable NoSQL queries, instantiated with MongoDB aggregation pipelines over schema-less document stores. We p

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