  A Natural Language Interface (NLI) facilitates users to pose queries to
retrieve information from a database without using any artificial language such
as the Structured Query Language (SQL). Several applications in various domains
including healthcare, customer support and search engines, require elaborating
structured data having information on text. Moreover, many issues have been
explored including configuration complexity, processing of intensive
algorithms, and popularity of relational databases, due to which translating
natural language to database query has become a secondary area of
investigation. The emerging trend of querying systems and speech-enabled
interfaces revived natural language to database queries research area., The
last survey published on this topic was six years ago in 2013. To best of our
knowledge, there is no recent study found which discusses the current state of
the art translations frameworks for natural language for structured and
non-structured query languages. In this paper, we have reviewed 47 frameworks
from 2008 to 2018. Out of 47, 35 were closely relevant to our work. SQL based
frameworks have been categorized as statistical, symbolic and connectionist
approaches. Whereas, NoSQL based frameworks have been categorized as semantic
matching and pattern matching. These frameworks are then reviewed based on
their supporting language, scheme of their heuristic rule, interoperability
support, dataset scope and their overall performance score. The findings stated
that 70% of the work in natural language to database querying has been carried
out for SQL, and NoSQL share 15%, 10% and 5% of languages like SPAROL, CYPHER
and GREMLIN respectively. It has also been observed that most of the frameworks
support English language only.
