Question Answering System for Rapid User Intent Mapping
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Solution Overview
Problem
Conventional systems face challenges in capturing and representing analytic intent in complex data spaces, leading to inefficiencies in data visualization and increased development times due to lack of specificity in analytic requirements, which limits re-usability across product lines.
Innovation Solution
A method involving a Mad-lib Sentence Structure (MLSS) template, Natural Language Processing (NLP) to discover data and context relationships, constructing a knowledge graph, and enriching it to form enriched phrasal entities, which are used to identify technical requirements and train models for an analytic task library, facilitating the creation of a question answering system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional analytic wizards are used to classify data, then data can be retrieved and displayed, but the system lacks specificity in analytic requirements leading to increased development times
Solution Approach 1:
The system performs preliminary action by automatically generating analytic requirements specifications before the actual data analysis work begins. The analytic requirement generation module creates detailed specifications including data sources, metrics, dimensions, and visualization requirements, which guides subsequent development activities and reduces rework.
Solution Approach 2:
The system enables self-service by allowing users to interact through natural language queries without requiring deep technical knowledge of data infrastructure. The natural language processing module interprets user intent and automatically translates it into technical analytic requirements, making the system accessible to business users while maintaining technical precision.
2Adaptability or versatility
If conventional systems are used for data visualization, then basic data display is achieved, but re-usability across product lines is limited
Solution Approach 1:
The system implements universality by creating a standardized analytic requirement specification format that can be reused across different product lines and data spaces. The specification template includes reusable components such as data source references, metric definitions, dimension structures, and visualization patterns that can be adapted to various analytical contexts without recreating them from scratch.
Solution Approach 2:
The system introduces an intermediary layer in the form of analytic requirement specifications that mediate between the complex data space infrastructure and the end-user needs. This specification layer abstracts the complexity of data sources, transformations, and visualizations into a standardized format that can be reused across different applications while maintaining fidelity to the underlying complex data structures.
3Measurement precision
If detailed analytic requirements are captured manually, then specificity is improved, but the process becomes time-consuming and less efficient
Solution Approach 1:
The system replaces the mechanical manual process of capturing analytic requirements with an automated natural language processing system. Users simply input their analytic intent in natural language, and the system automatically parses, structures, and validates the requirements, generating detailed specifications without manual intervention. This substitution maintains high precision while dramatically reducing the time investment required.
Solution Approach 2:
The system implements feedback mechanisms where the analytic requirement generation module continuously refines its interpretations based on user confirmations and corrections. The system presents generated requirements to users for validation, learns from their feedback, and automatically adjusts its parsing and generation processes to improve precision over time while maintaining efficiency.
Data Source
AI summary
A method for creating a question answering system includes receiving user stories, wherein each of the user stories is structured as a plurality of first phrasal entities within a template; applying a Natural Language Processing to discover first data relationships between the first phrasal entities and first context relationships between the first phrasal entities; constructing a knowledge graph that captures second data relationships and second contextual relationships of a plurality of second phrasal entities; enriching the KG by linking the first phrasal entities to the second phrasal entities to form enriched phrasal entities in the KG; receiving a selection of ones of the enriched phrasal entities for completing a story template; identifying a technical requirement based on the selection of the enriched phrasal entities; and training a model matching at least one of the user stories to the technical requirement.