Query Engine Selecting Best Answer from Diverse Sources
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Solution Overview
Problem
Current search engines are ineffective in providing simple, succinct answers to factual queries as they often rely on single sources, leading to limitations in question types they can answer and introduce ambiguity with expanded sources, including untrustworthy information.
Innovation Solution
A query engine that gathers factual information from diverse sources, scores possible answers, and selects the best answer based on confidence and relevance metrics, while providing a list of sources for user evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If search engines use a single source for facts, then the answer structure is simple, but the types of questions that can be answered are limited
Solution Approach 1:
The search engine is designed to query multiple diverse sources (encyclopedias, news articles, blogs, forums) rather than relying on a single source. This multi-source architecture enables the system to answer a broader range of question types including factual queries, popular culture questions, and product information, while maintaining a unified query processing framework
2Adaptability or versatility
If search engines expand the set of sources, then the coverage of question types increases, but the possibility of multiple contradictory or ambiguous answers increases
Solution Approach 1:
The system employs confidence scores for each potential answer based on source reliability and answer consistency. Users can provide feedback by selecting preferred answers or marking incorrect ones, which feeds back into the system to refine future scoring. This feedback mechanism helps the system learn from user preferences and improve answer accuracy over time
Solution Approach 2:
The system dynamically adjusts confidence score parameters based on source characteristics and answer properties. Different sources are assigned different weightings, and confidence scores are modified based on factors such as source authority, answer consistency across sources, and user interaction patterns. This parameter adjustment enables the system to handle contradictions by favoring more reliable sources
3Quantity of substance
If search engines expand the universe of sources, then the information coverage increases, but the introduction of untrustworthy or unknown reliability sources increases
Solution Approach 1:
The system introduces confidence scores as an intermediary mechanism between raw source data and final answers. This intermediary layer evaluates and ranks potential answers based on source reliability before presenting them to users. The confidence scoring system acts as a filter that mediates between the vast quantity of available information and the need for trustworthy, high-quality answers
4Loss of information
If search engines provide a list of matching Web pages, then the user can access detailed information, but the user must sort through pages to find the answer
Solution Approach 1:
The system extracts and presents the most relevant answer directly at the top of the search results, separating the best answer from the list of all matching pages. This extraction approach provides users with the most useful information immediately without requiring them to sort through multiple pages, while still maintaining links to source materials for those who want more detail
Data Source
AI summary
A method and system for selecting a best answer to a factual query. Possible answers to a factual query are identified. The possible answers are scored and the best scoring possible answers are compared to other possible answers to determine how well they are supported. The most supported answer is chosen to be presented to the user.