Dynamic Question-Answer Repository for Semantic Distinctness
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Solution Overview
Problem
Traditional question answering systems rely on static databases, which are incomplete and limited in information, leading to inadequate answers for users due to their reliance on manual generation and static knowledge resources.
Innovation Solution
A dynamic question-answer repository is implemented, where system-generated and user-submitted questions are evaluated for semantic distinctness using similarity functions, allowing for real-time updates and expansion of the repository, integrating question generation and answer retrieval processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a static database is used for question answering, then the system structure is simple and easy to maintain, but the information completeness is limited and answers are inadequate
Solution Approach 1:
The patent transforms the static question-answer database into a dynamic repository that automatically updates in real-time. The system continuously generates new questions from user queries and answers from knowledge bases, automatically adds semantically distinct questions to the repository, and maintains the database structure dynamically without manual intervention, thereby improving information completeness while managing complexity through automation.
Solution Approach 2:
The question-answer repository performs self-updates by automatically processing user questions, generating answers from knowledge bases, and adding new question-answer pairs to the repository without requiring manual curation. The system uses semantic analysis to identify distinct questions and automatically expands the database, enabling the system to serve and expand itself.
2Productivity
If manual generation of question-answer pairs is used, then the system is easy to control, but the productivity is low and the repository remains limited
Solution Approach 1:
The patent replaces the manual mechanical process of creating question-answer pairs with an automated computational system. The system uses natural language processing to extract questions from user queries, generates answers by querying knowledge bases, and automatically determines semantic distinctness using similarity functions, thereby dramatically increasing repository expansion speed while implementing full automation.
Solution Approach 2:
The system implements a feedback loop where user questions are processed to generate answers, which are then added to the repository. The system continuously monitors new queries against the repository, uses semantic analysis to identify gaps, and automatically expands the repository based on this feedback, creating a self-improving system that increases productivity through iterative automation.
3Speed
If the question-answer repository is updated in real-time, then the responsiveness to user queries improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by using semantic similarity functions to evaluate only the necessary subset of existing questions when a new query arrives, rather than processing the entire repository. The system adds only semantically distinct questions to the repository, performing computations only when needed to maintain responsiveness, thereby balancing speed with reasonable computational resource consumption.
4Manufacturing precision
If semantic distinctness evaluation is performed for all new questions, then the repository quality is maintained, but the processing complexity increases
Solution Approach 1:
The patent introduces semantic similarity functions as intermediaries to evaluate whether new questions are sufficiently distinct from existing ones. These functions compute semantic relationships between questions and serve as a filter, allowing the system to maintain high repository quality by including only semantically distinct questions while managing processing complexity through efficient similarity computation rather than exhaustive analysis.
Data Source
AI summary
Disclosed are some implementations of systems, apparatus, methods and computer program products for integrating question generation and answer retrieval in a question answer system. The system generates a question using a set of documents and determines whether it is semantically distinct from questions in a question-answer repository. After determining that the question is semantically distinct from questions in the question-answer repository, the system adds the question to the question-answer repository. Upon receipt of a user-submitted question, the system uses the question-answer repository to identify a semantically similar question. The system retrieves an answer corresponding to the identified question from the question-answer repository and provides the answer in response to the user-submitted question.