Natural Language Query Translation System
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Solution Overview
Problem
Current information retrieval technologies require users to have proficiency in database languages/schemas to formulate queries, posing a burden on organizations and limiting non-developers' ability to access and interpret data, especially in data-driven industries.
Innovation Solution
A centralized system that translates natural language queries into specific database queries, using components like message controllers, pre-processor libraries, grammar client libraries, and query generators to generate structured queries compatible with various database types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users directly query databases using SQL or database languages, then query precision and data retrieval accuracy are improved, but the ease of operation deteriorates because users need proficiency in database languages and schemas
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into database-specific SQL queries. This mediator component handles the conversion between natural language and structured query language, allowing users to query databases without learning SQL while maintaining query precision through automated translation and validation
2Manufacturing precision
If organizations hire employees proficient in database languages to formulate queries, then query formulation quality is improved, but the loss of time increases due to the burden on overstretched data teams
Solution Approach 1:
The patent enables business users to perform their own data queries without relying on specialized data team members. By providing automated natural language to SQL translation capabilities, the system allows users to independently formulate and execute queries, reducing the burden on data teams and eliminating waiting time for query assistance
3Ease of operation
If visual query builders are used to generate SQL queries, then ease of operation is improved, but device complexity increases due to the need for additional translation components and processing layers
Solution Approach 1:
The patent creates a universal natural language processing system that can translate queries across multiple database types and SQL dialects through a single interface. This multi-functional approach consolidates what would otherwise require separate visual query builders for each database system, reducing overall system complexity while maintaining ease of operation across diverse database environments
Data Source
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AI summary
Translating a natural language search query into a query language includes receiving a natural language query for a database, processing the natural language query to generate a modified text input, generating an entity tree based on the modified text input, including assigning one or more semantic markers to one or more words or one or more groups of words within the modified text input, wherein each semantic tag denotes a semantic class for each respective word or group or words, and converting the entity tree into the query language associated with the first database.