Attribute Annotation for Query Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing dataset analysis methods, such as SQL queries, are limited in abstracting user questions, making it difficult for users to extract meaningful information, leading to resource inefficiency and missed opportunities for organizational insights.
Innovation Solution
A method and system that annotate datasets with predefined attributes and provide question templates, allowing users to map questions to relevant attributes and patterns, thereby generating targeted database queries without requiring natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If SQL queries are used to access data, then data can be retrieved, but the ability to abstract user questions is limited and users have difficulty crafting queries
Solution Approach 1:
The patent introduces an intermediary layer between the user and the SQL query system. This intermediary automatically translates natural language questions into SQL queries by matching questions to pre-defined templates and attributes, eliminating the need for users to manually craft complex SQL syntax while maintaining data retrieval capability
Solution Approach 2:
The system performs preliminary actions by pre-defining question templates and attributes before user interaction. These templates capture common query patterns and relationships, allowing the system to automatically match and translate user questions without requiring real-time complex processing or user expertise in query formulation
2Measurement precision
If experts in SQL are used to obtain desired information, then accurate data can be retrieved, but resource expenditure increases
Solution Approach 1:
The system enables self-service by allowing any user to obtain accurate data through natural language questions without requiring SQL expertise. The automatic template matching and query generation system handles the complexity internally, providing expert-level data retrieval capability to all users equally without the need for specialized human resources
Solution Approach 2:
The intermediary system acts as an automated expert, translating user intent into precise SQL queries without human intervention. This eliminates the need to expend resources on hiring and training SQL experts while maintaining high data accuracy through the structured template matching approach
3Productivity
If traditional query methods are used, then data access is achieved, but opportunities for organizational insights are missed
Solution Approach 1:
The system provides multi-functionality by not only retrieving data but also automatically analyzing it against pre-defined business metrics and KPIs embedded in the question templates. This enables the system to perform multiple functions - data retrieval, analysis, and insight generation - within a single unified framework, increasing productivity while capturing organizational insights
Data Source
AI summary
A method and system for annotating attributes for use in responding to queries and using the annotated attributes. In one aspect, a dataset is annotated with attributes in predefined categories using a processor. Also using the processor, question templates are provided based on attributes. Each of the plurality of question templates corresponds to at least one query for the dataset. In another aspect, the method and system include receiving a question including a question template selected from predetermined question templates. In this aspect, the method and system also include mapping, using the processor, the question to at least one attribute of the plurality of attributes in the plurality of predefined categories and to at least one pattern for the at least one attribute.