Authors - Mohammad Kaif, Anshika Banyal, Rohitashwa Dey, Shashi Mehrotra Abstract - A Natural Language Interface (NLI) lets users ask questions to get data from a database without having to learn a new language like Structured Query Language. Structured data with text is needed for many applications in many fields, such as search engines, customer service, and healthcare. There are many problems that have been studied, such as the popularity of relational databases, the complexity of configuration, and the processing needs of algorithms. Translating plain language into database queries is only one of these problems. The resurgence of natural language to database queries research is driven by the increasing prevalence of querying systems and speech-enabled interfaces. The last poll on this topic was done six years ago, in 2013. As far as we know, there hasn't been any recent research that looks at the best natural language translation frameworks for both structured and unstructured query languages. We examined 47 frameworks from 2008 to 2018 in this report. 35 of the 47 were very useful for what we do. There are three kinds of SQL-based frameworks: connectionist, symbolic, and statistical. There are two types of NoSQL-based frameworks: semantic matching and pattern matching. After that, these frameworks are judged based on their language support, heuristic rule sys-tem, interoperability support, dataset scope, and overall performance. The results showed that 70% of the work to make natural language queries work with databases has been done for SQL. NoSQL languages like SPAROL, CYPHER, and GREMLIN only account for 15%, 10%, and 5% of the work, respectively. It has also been found that most of the frame-works only work with English.
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