Question Analysis via Linearized Sequence Topology Maps
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Solution Overview
Problem
Current knowledge base question answering technologies face challenges in accurately generating query graphs from complex questions, leading to poor accuracy in semantic representation and analysis.
Innovation Solution
A method that analyzes a question to obtain multiple linearized sequences, converts these sequences into network topology maps, calculates the semantic matching degree of each map to the question, and selects the map with the highest matching degree as the query graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If word sequence fusion method is used to generate query graph, then the process is simple, but the accuracy of query graph generation deteriorates for complex questions
Solution Approach 1:
The patent segments the query graph generation process into multiple candidate generation paths (different linearized sequences) and then selects the best match through semantic similarity comparison, rather than using a single fusion method
Solution Approach 2:
The patent changes the evaluation parameter from simple fusion to semantic similarity matching between candidate query graphs and the original question, allowing selection of the most accurate representation
2Measurement precision
If multiple linearized sequences are generated and converted to network topology maps, then the accuracy of question analysis is improved, but the complexity of the processing system increases
Solution Approach 1:
The system segments the analysis into multiple candidate linearized sequences that are independently processed into network topology maps, allowing parallel evaluation of different semantic interpretations
Solution Approach 2:
The system uses semantic similarity calculation as feedback to evaluate each candidate query graph against the original question, selecting the one with highest similarity to ensure accuracy
Data Source
AI summary
The present disclosure discloses a question analysis method, a device, a knowledge base question answering system and an electronic equipment. The method includes: analyzing a question to obtain N linearized sequences, N being an integer greater than 1; converting the N linearized sequences into N network topology maps; separately calculating a semantic matching degree of each of the N network topology maps to the question; and selecting a network topology map having a highest semantic matching degree to the question as a query graph of the question from the N network topology maps. According to the technology of the present disclosure, the query graph of the question can be obtained more accurately, and the accuracy of the question to the query graph is improved, thereby improving the accuracy of question analysis.