Semantic Expression Generation for Intelligent Question-Answer Systems
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Solution Overview
Problem
Existing intelligent question-answer systems face inefficiencies in maintaining knowledge bases due to the need for manual compilation of similar questions and high requirements for editing, leading to poor matching performance and low efficiency in semantic expression generation.
Innovation Solution
A method and apparatus for generating semantic expressions for standard questions in a knowledge base, involving data obtaining, semantic expression creation, detection, and deletion steps, which automatically select and validate phrases based on word classes and occurrence frequencies to improve matching accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual compilation of similar questions is used, then semantic expression generation can be performed, but the work efficiency is low and the editing requirements are high
Solution Approach 1:
The system automatically generates semantic expressions by analyzing user questions and matching them with standard questions from the knowledge base, eliminating the need for manual compilation. The algorithm independently performs segmentation, word class identification, and semantic expression generation without human intervention.
Solution Approach 2:
The patent replaces the manual mechanical process of compiling similar questions with an automated computational system that uses natural language processing algorithms, semantic analysis, and machine learning techniques to generate semantic expressions automatically.
2Reliability
If manual compilation of similar questions is used, then semantic expressions can be created, but the matching performance is poor
Solution Approach 1:
The system continuously optimizes semantic expression generation by analyzing matching results and user interactions. The algorithm learns from successful matches and unsuccessful attempts, adjusting its segmentation and word class identification strategies to improve matching performance over time.
Solution Approach 2:
The patent dynamically adjusts parameters such as segmentation granularity, word class thresholds, and semantic similarity weights based on performance metrics. This allows the system to optimize matching performance by changing computational parameters rather than relying on fixed manual rules.
3Productivity
If automated semantic expression generation is implemented, then generation efficiency is improved, but accuracy of semantic matching must be maintained
Solution Approach 1:
The system performs preliminary segmentation and word class identification on user questions before generating semantic expressions. This pre-processing step prepares the data in advance, allowing the main generation process to run efficiently while maintaining accuracy through pre-validated intermediate results.
Solution Approach 2:
The patent introduces intermediate representations such as word classes and segmented structures that bridge raw user questions and final semantic expressions. These intermediaries allow the system to process questions efficiently while preserving semantic meaning through structured transformation steps.
Data Source
AI summary
The present invention provides a method for generating a semantic expression for a standard question in a knowledge base. The knowledge base includes multiple standard questions, each standard question has multiple associated similar questions, and the method includes: for each standard question, obtaining multiple similar question segmentation results corresponding to the multiple similar questions of the standard question, where each similar question segmentation result includes word classes to which respective words in a corresponding similar question belong; for each standard question, selecting a phrase from an intersection of multiple similar question segmentation results of the standard question based on phrase occurrence frequencies, to form at least one semantic expression of the standard question, where each phrase includes a predetermined quantity of word classes; for all similar questions of all standard questions, performing standard question matching processing; for each semantic expression created for each standard question, determining whether the semantic expression is matched to at least one similar question of the standard question; and if yes, marking the semantic expression as a first state; or otherwise, marking the semantic expression as a second state; and deleting all semantic expressions in the second state.