Natural Language Querying on NoSQL Databases: Opportunities and Challenges [Vision Paper]

Natural language querying (NLQ) is an important research direction in both natural language processing and database communities. Over the past few years, using modern deep learning language generation and semantic parsing techniques to translate natural language questions to SQL queries, namely Text-to-SQL, has become a promising research topic. Despite the many limitations of using SQL queries for searching due to the predefined data structures and functionality of SQL databases, few attempts have been made beyond SQL query generation. Although there are many well-known, efficient, and scalable NoSQL databases and search engines, such as MongoDB and Elasticsearch, very little work has been devoted to developing NLQ tools for them and exploiting their potential. This gap motivates us to forge and explore the new research direction of NLQ on NoSQL databases. This vision paper aims to investigate the unique characteristics of the NoSQL database in the context of NLQ, examine the integration of NLQ with NoSQL databases, identify emerging research opportunities, and outline key challenges and potential research directions. We hope to inspire and stimulate further research investigation into adopting NoSQL databases for NLQ tasks.