NLP to Database Bridge Adapter for Query Translation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a need for improved technologies to provide flexible and easy-to-configure bridges between natural language processing (NLP) tools and database tools, as existing approaches require significant engineering effort and are often inadequate for multiple database deployments.
Innovation Solution
The disclosed technologies provide a bridge between the output of an NLP engine, in the form of a structured representation of a natural language query, and the exposed interface of a database query processor. This bridge extracts a sub-intent, maps it to a specific database interface, and transforms query parameters to conform with the target database interface, allowing for the construction of a database query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom NLP tools are built for specific database deployments, then the NLP tool can be tailored to meet specific requirements, but the engineering effort and time required increase significantly
Solution Approach 1:
The patent creates a universal adapter layer that enables a single NLP tool to work with multiple database systems through standardized interfaces. The adapter translates between different NLP output formats and database-specific query formats, eliminating the need to build custom NLP tools for each database deployment while maintaining full adaptability to specific requirements
Solution Approach 2:
The patent introduces an adapter as an intermediary component between the NLP tool and the database system. This adapter handles the translation and compatibility issues, allowing the NLP tool to remain generic while still meeting specific database deployment requirements through configurable adapter implementations
2Adaptability or versatility
If database tools are customized to match NLP outputs, then compatibility is improved, but the effort must be repeated when changing NLP tools
Solution Approach 1:
Instead of customizing database tools to match NLP outputs, the patent inverts the approach by creating adapters that translate NLP outputs into database-compatible formats. This reversal places the adaptation logic in the NLP side rather than the database side, making it easy to switch NLP tools without re-engineering database tools
Solution Approach 2:
The adapter framework provides universal compatibility with multiple NLP tools through a standardized interface. Each adapter is designed to work with specific NLP tools, but the overall system maintains the ability to switch between different NLP tools without modifying the database tools themselves
3Productivity
If NLP tools are built for specific database deployments, then performance can be optimized for that deployment, but the solution becomes inadequate for other database deployments
Solution Approach 1:
The patent creates a dynamic adapter system that can be configured and adjusted for different database deployments. The adapter layer allows the system to optimize performance for specific deployments through configuration rather than hard-coded customization, maintaining both high performance and broad adaptability across different database systems
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Methods and systems are disclosed for bridging a natural language processing (NLP) engine to a database engine, enabling natural language queries to be handled by the database engine. The NLP engine produces a query structure, containing an intent and one or more query parameters, for a natural language query. Rules are successively applied to the query structure. When a rule matches, a corresponding action is performed to identify a sub-intent, transform query parameters, or generate a filter string. The sub-intent is mapped to a query destination, which can be an intrinsic or extrinsic service offered by the data engine. A query string is constructed, including the query destination and any filter strings, and transmitted to the database engine to answer the original query. The disclosed technologies are flexible, easily reconfigurable, and future-proof. Variations and use cases are disclosed.