Natural Language Query Generation via UIMA Annotation
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Solution Overview
Problem
Current systems face challenges in generating structured queries from natural language text inputs, as users without programming knowledge struggle to formulate precise SQL requests for database management systems.
Innovation Solution
A method and system utilizing natural language processing (NLP) with the Unstructured Information Management Architecture (UIMA) to annotate and determine database operations and query elements from natural language text, thereby creating structured queries that can be executed by the database management system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users without programming knowledge attempt to formulate SQL queries directly, then query precision may be maintained, but ease of operation deteriorates significantly
Solution Approach 1:
The patent introduces a natural language processing system as an intermediary between the user and the database management system. Users input queries in natural language, and the NLP system automatically translates these into structured SQL queries, eliminating the need for users to learn SQL syntax while maintaining query precision through automated parsing and validation
2Ease of operation
If natural language processing is implemented to convert unstructured text to structured queries, then ease of operation improves, but device complexity increases
Solution Approach 1:
The patent implements a universal NLP processing pipeline that handles multiple types of natural language inputs (questions, statements, commands) and converts them all into standardized SQL query structures. This multi-functional approach consolidates various processing tasks into a single system that can handle diverse input formats without requiring separate processing mechanisms for each query type
3Productivity
If automated query generation from natural language is implemented, then productivity improves, but measurement precision may deteriorate due to interpretation errors
Solution Approach 1:
The patent incorporates feedback mechanisms where the NLP system analyzes the generated SQL queries against the original natural language input to ensure accuracy. The system can identify ambiguities, request clarification from users when needed, and validate that the generated queries correctly represent the intended information requests, thereby maintaining query accuracy while enabling automated processing
Data Source
AI summary
Generating structured queries from natural language text may include receiving, using a processor, a natural language text input directed to a database management system and, using the processor, performing natural language processing on the natural language text input using an Unstructured Information Management Architecture. The natural language processing may annotate the natural language text input according to a structure of the database management system. A database operation and query elements may be determined using a processor from the annotated natural language text input. A structured query may be created, using the processor, for the database management system that implements the database operation using the query elements.