Using Natural Language Text to Query Database with SQL
By transforming natural free-flowing query language into a structured query format, Natural Language Processing (NLP) allows question answering on a dataset. This is still one of the most challenging tasks in NLP, and it has gotten a lot of attention recently thanks to the availability of efficient language models.
The ability to convert natural language inquiries into Structured Query Language (SQL) has a wide spectrum of uses:
• Making data-driven insights available to those who don't know how to code
• lowering the time it takes to gain knowledge in a certain topic
• Increasing the value of the data that has been collected
As the amount of digital data has grown, a big amount of it has remained unanalyzed due to a lack of:
• infrastructure to keep it running
• processing techniques that are efficient
• There aren't enough individuals with the technical know-how to work with it.
The major barrier to implementing any deep-learning-based model is the lack of clean and tagged big datasets that could be used for training any natural language model.
The NLQ to SQL conversion in our technique consists of three steps:
• parsing and mapping
• a collection of logic and AI planning patterns
• mixing syntactical operators and database structure emulation
The fundamental concept is to construct complete and partial patterns and matches, which are then fed into SQL-based rules. We need to figure out how the algorithm will know whether a query contains numeric, date, or even timeline-based annotators. We used contraction mapping to ensure that the algorithm can recognize crucial phrases.
A lot of work is being done to solve the added hurdles of dealing with numerous tables utilizing different JOIN and sub-query approaches. This is especially important if we need to combine data from several sources to arrive at a solution. We may train the models to identify bespoke terminology as keywords in the case of custom solutions.