Converting Natural Language To SQL Queries Using LSTM

Abstract

In today\'s world, not everyone is familiar with using Structured Query Language (SQL). This makes it hard for users to understand or create complex SQL queries. What we need is an improved application with a smarter interface that can bridge the gap between novice users and databases. Databases are great at managing data, but to understand their structure, users have to learn SQL. This poses a challenge for non-experts who aren\'t well-versed in SQL. What they need is a system that allows them to interact with databases in natural language. The system should be capable of understanding and responding to natural language commands. To achieve this objective, we utilize a range of end-to-end deep learning models, as well as probability models like conditional random field. The ambiguity in natural language makes it exceedingly difficult to determine the exact meaning of every word, therefore it is a difficult process to map individual keywords to the description of the schema and the contents of the underlying database. Accurate predictions can help us avoid unnecessary trouble. If we apply machine learning tools, we can skip the complicated process.

Introduction

I. INTRODUCTION

Transforming natural language to SQL is a technology that aims to bridge the gap between human language and computer language. The language used to interact with relational databases is called SQL, or Structured Query Language. Although it is a strong tool for managing, organizing, and retrieving data, using it can be complicated and challenging for non-experts.

The objective of translating natural language to SQL is to develop a system that can comprehend human language and translate it into SQL. With this, accessing and modifying data in a database would be much simpler for non-experts. For instance, a user could enter a natural language query like "Show me all the customers who bought a product in the last month" and the system would translate that query into SQL and retrieve the necessary data without the user having to learn the syntax and structure of SQL.

NLP involves analyzing text using algorithms and statistical models to find patterns and connections between words and phrases.

Data analysis, business intelligence, and information retrieval are just a few of the many potential uses for translating natural language to SQL. This project aims to explore the techniques and tools used in transforming natural language to SQL, and to develop a system that can effectively perform this task.

II. RELATED WORK

The conversion of natural language to SQL has received a lot of attention in related research. Natural Language to SQL (NL2SQL) is the name of this area of study [1], which has been active for a while.

A set of manually generated rules were used in some of the earlier NL2SQL implementations to translate natural language queries to SQL queries [2]. However, the richness and variety of spoken language, along with its inability to scale to big datasets, hampered rule-based techniques.

In more modern NL2SQL methods, the mapping between natural language and SQL has been learned using machine learning techniques like deep learning and neural networks [3]. These methods have demonstrated potential for raising the precision and scalability of NL2SQL systems.

For instance, the Seq2SQL system, created by Microsoft Research and University of Washington academics in 2018 [4], employs a neural network to learn the correspondence between SQL queries and natural language inquiries. On a number of benchmark datasets, the system delivered cutting-edge performance.

Since then, a number of other studies on NL2SQL have been published, including investigations into data augmentation methods, semantic parsing, and transfer learning.

NL2SQL is a growing field of study with a lot of potential for real-world applications in data analytics, business intelligence, and information retrieval, among other fields.

III. METHODLOGY

LSTM (Long-shot-term-memory)  is a type of recurrent neural network which can be used in the natural language processing and sentimental analysis and to convert the given natural language text to the SQL query.

The memory cells in the LSTM network are responsible for storing long term information and the gating mechanism which includes the gates like input, output and forget gate which are intern used to control the flow of information in and out of the memory cells and each corresponds to a sigmoid function that outputs the value between 0 and 1.

The forget gate in the LSTM decides which information should be discarded from the memory cell based on the previous output and current input and the input gate decides which new information should be added to the memory based on current input and previous output and the output gate decides which information to output based on the current input and previous output.

Machine translation, sentiment analysis and speech recognition are few of the natural language processing tasks at which LSTM networks has demonstrated good results but they may need a lot of data to work well and they can be computationally expensive to train.

To map the natural language text to SQL queries we are using an LSTM based sequence to sequence model and This model is trained on a dataset of natural language and the corresponding SQL queries. We are using the LSTM to learn the relationships between the words in the natural language text to corresponding SQL queries.

The LSTM is used to encode the natural language query into a fixed length array or  vector which is then decoded by another LSTM to produce the SQL query. The decoder LSTM is trained to predict the next word in the SQL query based on the current state and the previous word and this is repeated until the entire SQL query is generated.

IV. IMPLEMENTATION

The proposed model involves multiple steps to convert natural language to SQL query. Firstly, a database is used to train the model, followed by preparing the data by paraphrasing the natural language text and generating corresponding SQL text. The model first converts the input data into vectors of fixed length and the LSTM model is prepared by using encoder and decoder. The encoder assigns a value to the paraphrased natural language text which is then decoded to SQL words by the decoder. Finally, the SQL query is generated and returned by the model. The detailed steps involved are:

Pre-process the data: The data needs to be cleaned and pre-processed to remove any noise, inconsistencies, or irrelevant information. This involves tokenizing the input text, converting it to lower case, removing stop words, and stemming or lemmatizing the words.

Prepare the data:  the data which was pre-processed needs to be converted into vectors of fixed length and then it is encoded with numbers and this involves <PAD>, <UNK>, <SOS> and <EOS>. These encoded vectors are used to train the LSTM model.

Defining LSTM architecture: This step involves to define the number of layers, the no of nodes in each layer and the activation function etc. 

Training the LSTM: The pre-processed data is used to train the LSTM model and during the training the model updates it weights in the neural network to minimize the loss function. The LSTM model may use back propagation and gradient descent to adjust its weights to maximize its output accuracy.

Evaluation of the model: The trained model is evaluated by testing it on the testing data that was separated at the time of processing of data.

Conclusion

Transforming natural language to SQL is an exciting field that has the potential to revolutionize the way we interact with data. By bridging the gap between human language and computer language, it has the potential to make it much easier for non-experts to access and manipulate data in a database. The development of natural language processing (NLP) technology will play a crucial role in this process. As NLP technology continues to evolve, it will become more accurate and efficient, making it easier to transform natural language to SQL. Additionally, the integration of machine learning algorithms and large language models (LLMs) will further enhance the accuracy and efficiency of this process. The potential applications of this technology are vast, including improved data analysis, enhanced decision-making capabilities, and increased productivity. It is clear that this technology has the potential to revolutionize the way we interact with data, and it will be exciting to see how it continues to evolve in the years to come.

References

[1] A survey paper on Natural Language to SQL systems: Zhong, V., & Liu, Z. (2019). A survey on natural language processing for databases. ACM Computing Surveys (CSUR), 52(4), 1-34. [2] Rule-based NL2SQL techniques: Popescu, A. M., & Etzioni, O. (2005, June). Extracting product features and opinions from reviews. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (pp. 339-346). [3] Modern machine learning-based NL2SQL techniques: Xu, K., Wu, L., Wang, Z., Chen, Y., & Wu, D. (2017). SQLNet: Generating structured queries from natural language without reinforcement learning. arXiv preprint arXiv:1711.04436. [4] The Seq2SQL system: Zhong, V., & Liu, Z. (2019). Seq2SQL: Generating structured queries from natural language using reinforcement learning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 2372-2382). [5] Other recent studies on NL2SQL: Agarwal, S., Hamborg, F., & Lehmann, J. (2021). Data augmentation for question-to-SQL generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4504-4514). [6] Kushman, N., Erk, K., & Liang, P. (2014). Compositional semantic parsing on semi-structured tables. Transactions of the Association for Computational Linguistics, 2, 143-154. [7] Dong, L., Wei, F., Zhou, M., & Xu, K. (2018). Neural semantic parsing with type constraints for semi-structured tables. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1035-1045).

Copyright

Copyright © 2023 Venkata Adithya Vytla, Dr. Sreedhar Bhukya, Dr. T. Vijaya Saradhi, Saicharan Pabbathi, K V Venkata Ramana Reddy, B. Vasundhara Devi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.