Towards a Natural Language Query Processing System

TL;DR

This paper presents a natural language query system that uses a graph database as an intermediary to translate user queries into structured database queries, achieving high accuracy in a restaurant dataset.

Contribution

Introduces a novel approach using a graph database as a middle layer for natural language to SQL translation in relational databases.

Findings

Achieved 90% accuracy in query translation.

Demonstrated effectiveness on a restaurant dataset.

Bridged natural language queries with structured databases.

Abstract

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.