Enhanced Natural Language Interface for Web-Based Information Retrieval

Database application is at the core of most web application systems such as web-based email, source codes repository management, public scientific data repository management, news portals, and publication repository of various fields. However, the usage of these database systems for data and information retrieval is severely limited because of lacking support for processing search queries expressed in a natural language (NL). Most web interfaces for databases today only take search queries entered in some form of logical combination of keywords or text strings, which restrict the scope and depth of what a web user really wants to search for, even though natural language based data or information retrieval has made significant advances in recent years. To overcome or at least to alleviate such limitation in web information services, we propose in this article an improved neural model based on an existing framework IRNet for NL query of databases, in which a representation of Gated Graph Neural Network (GGNN) is introduced to encode the database entities and relations. We also represent and use the database values in the prediction model to identify and match table and column names for automatic synthesize a correct SQL statement from a query expressed in a NL sentence. Experiments with a public dataset demonstrates the promising potential of our approach.

Automated summary

In this article, an improved neural model based on the IRNet framework is proposed for NL query of databases, which encodes database entities and relations using a representation of Gated Graph Neural Network (GGNN). Database values are also represented and used in the prediction model to automatically synthesize a correct SQL statement from a query expressed in a NL sentence. Experiments with a public dataset demonstrate the promising potential of the approach.