Semantic Question Matching for Accurate Answer Retrieval
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Solution Overview
Problem
Intelligent question-answering platforms struggle to accurately understand user questions, leading to low accuracy in providing answers.
Innovation Solution
A method and apparatus that utilize a question-answering database to match user questions with candidate questions based on vector similarity and semantic analysis, ensuring that answers are derived from questions with identical semantics, and if not found, utilize a knowledge base or large language model to provide accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword matching is used to search the question-answering database, then the search speed is fast, but the semantic accuracy is low leading to incorrect answer matching
Solution Approach 1:
The patent introduces vector embeddings as an intermediary representation layer between the question input and the database search. The question and candidate questions are transformed into vector representations, allowing the system to capture semantic meaning rather than relying on simple keyword matching. This intermediary vector space enables accurate semantic matching while maintaining system efficiency through optimized vector search algorithms.
Solution Approach 2:
The patent transforms the matching parameters from traditional keyword-based string comparison to vector-based semantic similarity measurement. By changing the representation parameters from discrete keywords to continuous vector embeddings, the system achieves more accurate semantic matching. The vector similarity computation allows for nuanced understanding of question meaning, resolving the contradiction between matching accuracy and system complexity.
2Reliability
If vector database with semantic analysis is used to match questions, then the answer accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing vector embeddings for all candidate questions in the database before runtime queries. This pre-processing step creates a ready-to-search vector index, so that when a user question arrives, the system only needs to compute its vector representation and perform efficient similarity search against the pre-built index. This significantly reduces the processing time during actual question-answering operations while maintaining high answer accuracy through semantic matching.
3Reliability
If multiple candidate questions are retrieved and semantically verified, then the answer reliability is improved, but the number of processing steps increases
Solution Approach 1:
The patent applies partial action by retrieving only the top-k most similar candidate questions based on vector similarity, rather than examining all questions in the database. This partial retrieval approach focuses computational resources on the most promising candidates, achieving high answer reliability through semantic verification of a limited set of relevant questions. The system balances thoroughness with efficiency by verifying semantics only for the most relevant matches.
Data Source
AI summary
The present application discloses a question-answering method. The method includes: acquiring a to-be-processed question sentence; determining, from a question-answering database, at least one candidate question sentence matching the to-be-processed question sentence, wherein the question-answering database includes the at least one candidate question sentence and an answer corresponding to each of the at least one candidate question sentence; determining whether semantics of the to-be-processed question sentence and semantics of the at least one candidate question sentence are same; and in response to a candidate question sentence with same semantics as the to-be-processed question sentence exists in the at least one candidate question sentence, acquiring, from the question-answering database, an answer corresponding to the candidate question sentence with the same semantics as the to-be-processed question sentence, and taking the answer as an answer corresponding to the to-be-processed question sentence.