Context-Aware Question Answering System for Business Intelligence
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Solution Overview
Problem
Business Intelligence (BI) systems require technical knowledge for querying data, making it inaccessible to non-expert users, and conventional question answering (QA) systems struggle to translate natural language queries into relevant formal representations for BI data.
Innovation Solution
A context-aware QA system that uses semantic abstraction based on BI semantic universes to translate natural language queries into technical queries, enabling direct answers to BI queries by parsing and matching queries to predefined patterns, and generating answers from structured data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If BI systems use structured data and technical query languages (SQL, SPARQL, MDX), then data retrieval accuracy and system reliability are improved, but user accessibility and ease of operation deteriorate because non-expert users cannot formulate queries
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between users and the BI system's structured query interface. This intermediary automatically translates user-friendly natural language questions into formal query languages (SQL, SPARQL, MDX), allowing non-expert users to access structured data without learning technical query syntax while maintaining data retrieval accuracy
Solution Approach 2:
The system segments the complex query formulation process into two distinct layers: a user-facing natural language interface layer and a backend structured query execution layer. This segmentation allows each layer to operate independently with its own optimization strategies, improving both user accessibility and data retrieval reliability
2Adaptability or versatility
If conventional QA systems process unstructured data (documents, text corpus), then information retrieval flexibility is improved, but answer precision and manufacturing precision deteriorate because answers must be manually extracted from unstructured sources
Solution Approach 1:
The patent merges the advantages of unstructured data processing (flexibility in query formulation) with structured data processing (precision in answer generation). By combining natural language processing capabilities with access to structured BI data sources, the system achieves both flexible information retrieval and precise answer generation simultaneously
Data Source
AI summary
A question is received to be answered by a question answering (QA) system. The question may be a business intelligence question that is expressed in a natural language. The question is parsed. The parsed question is matched to a pattern from a number of patterns. A technical query associated with the matched pattern is processed to retrieve data relevant to the question from a number of data sources. The QA system generates an answer to the question based on retrieved data. In one aspect, the QA system generates answers based contextual information.