Neural Network Translator for Natural Language Database Queries
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Solution Overview
Problem
The increasing complexity and size of databases make it impractical for human operators to efficiently search, retrieve, and analyze data, and existing translation technologies face challenges in facilitating user-friendly interaction between humans and computers for database access and management.
Innovation Solution
A method involving a two-stage training process for an automatic translator using neural networks to translate natural language into artificial language, where the first stage improves similarity between artificial language inputs and outputs, and the second stage enhances the translation of natural language inputs into artificial language outputs, enabling efficient database query formulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If databases increase in size and complexity to handle large volumes of data, then data storage capacity and analytical capability improve, but the practicality of human operators searching and retrieving data deteriorates
Solution Approach 1:
The patent introduces a natural language processing intermediary system that translates human-readable queries into database-specific query languages. This mediator layer allows users to interact with complex databases using natural language rather than requiring knowledge of complex query syntax, thereby maintaining ease of operation while handling large and complex data volumes.
Solution Approach 2:
The patent replaces manual mechanical operations of data searching and querying with an automated natural language processing system. The system automatically parses, understands, and translates natural language queries into executable database queries, eliminating the need for manual construction of complex queries and maintaining operational simplicity despite increasing data complexity.
2Ease of operation
If translation technology is simplified for user-friendliness, then accessibility to database operations improves, but translation accuracy and reliability deteriorate
Solution Approach 1:
The patent segments the translation process into multiple distinct stages: natural language input processing, semantic interpretation, query language generation, and query validation. Each stage handles specific aspects of the translation, allowing the system to maintain simplicity in user interaction while ensuring accuracy through systematic processing at each stage.
Solution Approach 2:
The patent implements feedback mechanisms where the system validates generated queries against the target database schema and provides corrections or clarifications to users when ambiguities are detected. This feedback loop ensures translation accuracy while maintaining user-friendly interaction by guiding users through necessary refinements rather than requiring perfect initial input.
Data Source
AI summary
Described systems and methods allow an automatic translation from a natural language (e.g., English) into an artificial language such as a structured query language (SQL). In some embodiments, a translator module includes an encoder component and a decoder component, both components comprising recurrent neural networks. Training the translator module comprises two stages. A first stage trains the translator module to produce artificial language (AL) output when presented with an AL input. For instance, the translator is first trained to reproduce an AL input. A second stage of training comprises training the translator to produce AL output when presented with a natural language (NL) input.