Transformer Query Translation Using Cross-Attention Schema Alignment

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Solution Overview

Problem

Existing query systems struggle to accurately convert natural language queries into structured queries due to the need for expert knowledge and inefficiencies in interpreting user intentions, particularly when dealing with complex database structures.

Innovation Solution

A natural language query processing apparatus using a transformer neural network that encodes natural language queries and relational databases into meaningful representations, employing cross-attention mechanisms to generate structured queries by separating schema and natural language processing, allowing for unsupervised pre-training of schema encoders and masked column modeling to enhance accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional natural language query processing is used, then the system can process queries, but the conversion into structured queries is not accurate enough

Engineering Contradiction:

Improvequery conversion accuracyVSAvoiduser intention interpretation reliability

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent segments the query processing into distinct modules: natural language processing module, schema processing module, and cross-attention mechanism. This segmentation allows each module to specialize in specific tasks, improving overall accuracy in converting natural language queries to structured queries while maintaining reliable user intention interpretation.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The cross-attention mechanism serves as an intermediary between the natural language processing results and schema relationships. It mediates the interaction between these two components, enabling accurate alignment and conversion while preserving the semantic meaning and user intentions throughout the processing pipeline.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If expert knowledge about query standards is required, then structured queries can be generated accurately, but user operation becomes difficult

Engineering Contradiction:

Improvestructured query generation accuracyVSAvoiduser query input ease

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system performs self-service by automatically converting natural language queries to structured queries without requiring user expertise. The neural network models and cross-attention mechanisms handle the complex conversion process autonomously, allowing users to input simple natural language while receiving accurate structured query generation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of manual query construction with an automated neural network-based system. Instead of requiring users to manually construct queries following specific standards, the system uses transformer-based models to automatically generate structured queries from natural language inputs, maintaining accuracy while eliminating the need for user expertise.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If complex database schemas are processed, then comprehensive query results can be obtained, but processing complexity increases

Engineering Contradiction:

Improveschema information coverageVSAvoidprocessing complexity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The patent segments the schema processing into independent modules that handle different aspects of schema information separately. This allows comprehensive processing of complex database schemas while maintaining manageable complexity through modular architecture, where each module processes specific schema elements before integrating them through cross-attention mechanisms.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The cross-attention mechanism acts as an intermediary that simplifies the processing of complex schema information. It processes and aligns schema relationships with natural language queries, reducing the complexity burden on individual processing components while maintaining comprehensive coverage of the schema structure.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12505098B2Apparatus and method for processing natural language query about relational database using transformer neural network

Publication Date: 2025.12.23 POSTECH ACADEMY INDUSTRY FOUNDATION

AI summary

Disclosed is a natural language query processing apparatus comprising, a processor that receives a natural language query input by a user and generates a structured query based on the natural language query, wherein the processor, when generating the structured query based on the natural language query, generates the structured query using a natural language processing result for the natural language query, a schema relationship extracted based on a relationship between sub-databases in a database related to the natural language query, and a cross-attention result generated between the natural language processing result and the schema relationship.