Natural Language Query Translation to Structured Data
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Solution Overview
Problem
Current systems face challenges in translating natural language queries into structured data queries, particularly for non-technical users, due to difficulties in comprehending user intent and rigidity in system configuration, leading to limited accessibility of 'dark data' and suboptimal decision support.
Innovation Solution
A method that extracts entities from natural language queries, correlates them with generic variables, and uses natural language classifiers to associate the queries with structured question types, replacing entities with specific data to form structured data queries, which can be validated and submitted to structured data systems like SQL databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing is used to enable non-technical users to query structured data systems, then ease of operation is improved, but system complexity increases due to the need for multiple natural language classifiers and entity extraction mechanisms
Solution Approach 1:
The system segments the natural language processing task into distinct components: entity extraction, generic query generation, and structured question type classification. Each component is handled by specialized modules that work sequentially, making the overall complex system manageable through division of labor among independent functional units
Solution Approach 2:
The patent introduces an intermediary processing layer that translates natural language queries into structured representations. This intermediary layer includes entity extraction mechanisms and generic query generators that bridge the gap between user-friendly natural language and structured data system queries, reducing the perceived complexity for end users
2Measurement precision
If multiple natural language classifiers are used to accurately interpret user intent, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary entity extraction and generic query generation before passing the processed input to multiple natural language classifiers. This preliminary processing reduces the complexity of the classification task and allows parallel evaluation of multiple classifiers, improving accuracy while minimizing additional processing time
Solution Approach 2:
The patent employs multiple natural language classifiers in parallel, using an excessive number of classification passes to ensure high accuracy in user intent comprehension. The system accepts the additional processing time as a necessary trade-off to achieve reliable interpretation of user intent, particularly for complex queries
3Adaptability or versatility
If entities are extracted and correlated with generic variables to enable flexible querying, then adaptability is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The system changes parameters by extracting entities from natural language queries and correlating them with generic variables. This transformation allows the same generic query template to adapt to different specific queries by simply changing the entity-variable mappings, providing flexibility without requiring complex reconfiguration of the underlying system
Data Source
AI summary
A natural language query (NLQ) is translated to a structured data query (e.g., a SQL statement) by extracting entities from the NLQ and replacing them with generic variables to form a generic query. The generic query is associated with a structured question type which includes structured data variables using natural language classifiers (NLCs). Specific data is inserted in the structured question type in relation to the structured data variables based on the extracted entities to form the structured data query. An ensemble of NLCs trained with different ground truths can be used to yield multiple candidate question types. One of the candidate question types is selected based on confidence levels. The multiple NLCs can include an NLC which is optimized according to a focus of the generic query. For example, an NLC can be optimized for a specific data structure (such as SQL), or for comparative queries.