A Review of NLIDB With Deep Learning: Findings, Challenges and Open Issues
- Title
- A Review of NLIDB With Deep Learning: Findings, Challenges and Open Issues
- Author
- Scott Uk-Jin Lee
- Keywords
- Aerospace; Bioengineering; Communication, Networking and Broadcast Technologies; Components, Circuits, Devices and Systems; Computing and Processing; Engineered Materials, Dielectrics and Plasmas; Engineering Profession; Fields, Waves and Electromagnetics; General Topics for Engineers; Geoscience; Nuclear Engineering; Photonics and Electrooptics; Power, Energy and Industry Applications; Robotics and Control Systems; Signal Processing and Analysis; Transportation; Deep learning; Structured Query Language; Databases; Task analysis; Pipelines; Machine translation; Electronic mail; Text to SQL; natural language processing; NLIDB; database; natural language; deep learning; structured language
- Issue Date
- 2022-01
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Citation
- IEEE ACCESS, v. 10, Page. 14927-14945
- Abstract
- Relational databases are storage for a massive amount of data. Knowledge of structured query language is a prior requirement to access that data. That is not possible for all non-technical personals, leading to the need for a system that translates text to SQL query itself rather than the user. Text to SQL task is also crucial because of its economic and industrial value. Natural Language Interface to Database (NLIDB) is the system that supports the text-to-SQL task. Developing the NLIDB system is a long-standing problem. Previously they were built based on domain-specific ontologies via pipelining methods. Recently a rising variety of Deep learning ideas and techniques brought this area to the attention again. Now end to end Deep learning models is being proposed for the task. Some publicly available datasets are being used for experimentation of the contributions, making the comparison process convenient. In this paper, we review the current work, summarize the research trends, and highlight challenging issues of NLIDB with Deep learning models. We discussed the importance of datasets, prediction model approaches and open challenges. In addition, methods and techniques are also summarized, along with their influence on the overall structure and performance of NLIDB systems. This paper can help future researchers start having prior knowledge of findings and challenges in NLIDB with Deep learning approaches.
- URI
- https://ieeexplore.ieee.org/document/9696343https://repository.hanyang.ac.kr/handle/20.500.11754/171071
- ISSN
- 2169-3536
- DOI
- 10.1109/ACCESS.2022.3147586
- Appears in Collections:
- ETC[S] > 연구정보
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