TY - GEN
T1 - Natural Language Querying on NoSQL Databases
T2 - 2024 IEEE International Conference on Big Data, BigData 2024
AU - Zhang, Wenlong
AU - Shi, Tian
AU - Wang, Ping
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Natural language querying
KW - NoSQL database
UR - http://www.scopus.com/inward/record.url?scp=85218012528&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85218012528&partnerID=8YFLogxK
U2 - 10.1109/BigData62323.2024.10825998
DO - 10.1109/BigData62323.2024.10825998
M3 - Conference contribution
AN - SCOPUS:85218012528
T3 - Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
SP - 3353
EP - 3357
BT - Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
A2 - Ding, Wei
A2 - Lu, Chang-Tien
A2 - Wang, Fusheng
A2 - Di, Liping
A2 - Wu, Kesheng
A2 - Huan, Jun
A2 - Nambiar, Raghu
A2 - Li, Jundong
A2 - Ilievski, Filip
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
Y2 - 15 December 2024 through 18 December 2024
ER -