QA System Weighting Traditional and Crowd Sources
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Solution Overview
Problem
QA systems trained from traditional sources may not provide up-to-date answers due to the time-dated information in published textbooks and journals, which are updated infrequently.
Innovation Solution
A QA system ingests traditional sources and crowd-based sources, calculating weightings for traditional terms based on crowd-based metadata to provide relevant answers to questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If QA systems are trained from traditional sources (textbooks, journals), then the answers are based on authoritative and accurate information, but the information becomes time-dated and not up-to-date due to infrequent publication updates
Solution Approach 1:
The patent combines traditional authoritative sources with crowd-based real-time sources into a unified QA system. The system merges structured traditional corpora with unstructured crowd-generated content, allowing it to leverage both the accuracy of traditional sources and the timeliness of crowd-based sources simultaneously.
Solution Approach 2:
The patent introduces a weighting mechanism as an intermediary that mediates between traditional source reliability and crowd-based source timeliness. This weighting system evaluates and balances contributions from different sources, allowing the QA system to dynamically adjust the influence of each source type based on the specific query context.
2Loss of time
If QA systems use crowd-based sources for real-time information, then the timeliness of answers improves, but the accuracy and reliability may decrease due to unverified crowd-generated content
Solution Approach 1:
The patent implements a feedback mechanism where crowd-based information is continuously evaluated and weighted against traditional authoritative sources. The system provides feedback loops that assess the quality and reliability of crowd-generated content, adjusting weights dynamically to maintain accuracy while preserving timeliness advantages.
Solution Approach 2:
The patent changes the parameter of source reliability by introducing dynamic weighting that adjusts the trust level assigned to different sources. Instead of treating all sources equally, the system modifies the reliability parameter based on source type, content quality, and contextual relevance, allowing flexible balancing between accuracy and timeliness.
3Reliability
If QA systems integrate both traditional and crowd-based sources, then the system complexity increases, but the ability to provide up-to-date and accurate answers improves
Solution Approach 1:
The patent segments the QA system into distinct modules: one for processing traditional authoritative sources and another for processing crowd-based sources. Each segment handles specific source types with dedicated processing logic, making the overall complex system manageable through modular organization and independent optimization of each segment.
Data Source
AI summary
An approach is provided in which a QA system ingests traditional sources, which includes traditional terms, into a domain dictionary. Next, the QA system ingests crowd-based sources that include crowd-based terms and corresponding crowd-based metadata. In turn, the QA system calculates weightings pertaining to the traditional terms based upon the crowd-based metadata. When the QA system receives a question from a requestor that includes question terms, the QA system identifies an answer to the question based on the calculated weightings pertaining to the traditional terms that are relevant to the question terms.