Natural Language Interface for Database Query Translation
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Solution Overview
Problem
Non-technical business users lack access to complex data stored in databases due to the opaqueness of big data, requiring scalable methods for self-service access without intermediaries.
Innovation Solution
A natural language interface system using deep learning that receives inquiries, performs named entity recognition, intent classification, and semantic parsing to generate database queries, allowing translation into various underlying technologies and abstracting disparate database systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If database systems use complex query languages like SQL, then data access precision and control are improved, but ease of operation deteriorates because non-technical users cannot write queries
Solution Approach 1:
The patent introduces an intermediary natural language processing system that translates between user-friendly natural language and complex SQL queries. This intermediary layer allows non-technical users to interact with databases using simple language while the system automatically generates the precise SQL queries needed, resolving the contradiction between access precision and ease of operation.
Solution Approach 2:
The patent replaces the mechanical requirement of manually writing and syntax-checking SQL queries with an automated natural language processing system. Users speak or type natural language instead of mechanically constructing SQL statements, and the system automatically handles the complex query generation, eliminating the barrier to entry while maintaining query precision.
2Device complexity
If database systems are designed for technical users with standardized interfaces, then device complexity is reduced, but adaptability deteriorates because they cannot serve non-technical users effectively
Solution Approach 1:
The patent implements a universal interface that serves multiple user types (technical and non-technical) through a single natural language processing system. This multi-functional approach allows the database system to adapt to different user capabilities without requiring separate complex interfaces, thereby increasing adaptability while managing complexity through a unified architecture.
Solution Approach 2:
The system enables non-technical users to access databases independently without requiring intermediaries like data analysts or programmers. By providing self-service capabilities through natural language processing, the system expands its user base adaptability while maintaining reasonable complexity through automated translation rather than human mediation.
3Productivity
If non-technical users want self-service access to data, then productivity is improved, but device complexity increases due to the need for natural language processing systems
Solution Approach 1:
The natural language processing system acts as an intermediary that handles the complexity of query generation, entity recognition, and SQL translation. This intermediary absorbs the processing complexity, allowing users to focus on expressing their data needs in natural language while the system manages the computational complexity of converting those requests into executable queries.
Solution Approach 2:
The patent replaces manual query construction mechanics with automated natural language processing mechanics. The system uses machine learning models, entity recognition algorithms, and automated SQL generation to handle processing complexity, substituting automated computational processes for manual user effort and thereby improving productivity while concentrating complexity in the processing layer rather than the user interface.
Data Source
AI summary
An embodiment of the present invention is directed to combining natural language processing with constrained grammar defined pattern matching to advantageously allow a system to translate natural language questions into any number of underlying technologies. By using a unique intermediate parse tree representation, the disclosed embodiments are able to instantiate a corresponding data store adapter for each given query, which may be for a relational database or a no-SQL database, for example. The ability to abstract underlying storage technology advantageously allows the management of disparate database systems, which is not possible using existing methods and technology.