  Tackling the information retrieval gap between non-technical database
end-users and those with the knowledge of formal query languages has been an
interesting area of data management and analytics research. The use of natural
language interfaces to query information from databases offers the opportunity
to bridge the communication challenges between end-users and systems that use
formal query languages. Previous research efforts mainly focused on developing
structured query interfaces to relational databases. However, the evolution of
unstructured big data such as text, images, and video has exposed the
limitations of traditional structured query interfaces. While the existing web
search tools prove the popularity and usability of natural language query, they
return complete documents and web pages instead of focused query responses and
are not applicable to database systems. This paper reports our study on the
design and development of a natural language query interface to a backend
relational database. The novelty in the study lies in defining a graph database
as a middle layer to store necessary metadata needed to transform a natural
language query into structured query language that can be executed on backend
databases. We implemented and evaluated our approach using a restaurant
dataset. The translation results for some sample queries yielded a 90% accuracy
rate.
