Natural Language Query Decomposition for Database Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Formulating suitable queries for databases can be complex and requires knowledge of syntax and database variables, making it difficult for users to retrieve relevant data using natural language.
Innovation Solution
A system utilizing a natural language processing module and a classifier module to analyze and decompose user queries into elements, determine query types, and generate structured database queries, allowing for data retrieval and processing to provide relevant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users formulate queries using natural language, then the ease of operation is improved, but the precision of data retrieval deteriorates due to lack of syntax knowledge
Solution Approach 1:
The patent introduces an intermediary system comprising NLP modules and query generation modules that translate natural language queries into structured database queries. The NLP module parses the natural language input, extracts entities and relationships, and the query generation module converts these into precise SQL or other database query languages, thereby mediating between user-friendly natural language and precise database syntax.
Solution Approach 2:
The system changes the parameter of query formulation from requiring syntax knowledge to accepting natural language. By implementing automated query generation that transforms natural language parameters into structured query parameters, the system maintains retrieval precision while improving ease of operation.
2Measurement precision
If users formulate queries with proper syntax and knowledge of database variables, then the precision of data retrieval is improved, but the ease of operation deteriorates due to complexity requirements
Solution Approach 1:
The system enables self-service query formulation by automatically generating structured queries from natural language input. The NLP module and query generation module work together to create syntactically correct database queries without requiring user intervention or knowledge of database syntax, making the system serve itself in terms of query construction.
3Ease of operation
If a system translates natural language to structured queries using NLP and classifier modules, then the ease of operation is improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex translation task into distinct functional modules: an NLP module for parsing natural language, a classifier module for determining query types, and a query generation module for creating structured queries. This segmentation allows each module to specialize in a specific aspect of the translation process, managing overall system complexity through modular design.
Solution Approach 2:
The system implements universal modules that can handle multiple types of queries and natural language inputs. The NLP module and query generation module are designed to be multi-functional, accommodating various query types (selection, aggregation, joining, etc.) and natural language patterns, thereby reducing the need for separate specialized components.
4Measurement precision
If the system decomposes and analyzes user queries into elements and determines query types, then the precision of data retrieval is improved, but the loss of time increases due to additional processing steps
Solution Approach 1:
The system performs preliminary action by pre-compiling and storing query templates and patterns. When a natural language query is received, the NLP module quickly matches it against pre-defined patterns, and the classifier module uses pre-trained models to determine query types, avoiding the need for complex real-time analysis and reducing processing time while maintaining precision.
Data Source
AI summary
There is provided a method for retrieving data, the method being executed by at least one processor, the method comprising: receiving a natural language query; decomposing the natural language query into query elements; generating, by providing the query elements to at least one classifier, a query type characterizing the natural language query; identifying a subset of the query elements based on the query type; generating a structured database query including the subset of the query elements; and retrieving data based on the structured database query.