Knowledge Graph Node Path Generation for Question Bank Expansion
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Solution Overview
Problem
Existing question banks lack a method for generating new questions, relying solely on classification, presentation, searching, and correction of existing questions, which limits their expansion and enhancement.
Innovation Solution
A knowledge graph is created by parsing question-answering steps of existing questions into nodes representing steps, knowledge points, and questioning styles, allowing for the generation of new questions based on user requests by acquiring a node path from the graph that corresponds to the requested knowledge point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If question banks rely solely on classification, presentation, searching, and correction of existing questions, then the processing of existing questions is efficient, but the expansion and enhancement of the question bank is limited
Solution Approach 1:
The system performs preliminary actions by constructing a knowledge graph from existing questions before new question generation is needed. The knowledge graph pre-organizes question structures, knowledge points, and answering steps into a reusable framework that enables rapid new question generation without requiring full re-processing of existing content each time
Solution Approach 2:
The system generates new questions by copying and recombining structural elements from existing questions stored in the knowledge graph. Question templates, knowledge point relationships, and answering step patterns are replicated and adapted to create novel questions that maintain the quality and structure of original content
2Adaptability or versatility
If a knowledge graph is constructed by parsing all existing questions into nodes and edges, then new question generation capability is enabled, but the system complexity increases
Solution Approach 1:
The system segments the question bank into discrete, standardized nodes (questions, knowledge points, answering steps) that can be independently processed and recombined. This segmentation transforms the monolithic question bank into modular units that form the knowledge graph, making construction and maintenance more manageable despite the increased complexity
3Manufacturing precision
If question-answering steps are parsed into detailed nodes with knowledge points and questioning styles, then the precision of generated questions improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs the time-consuming parsing and analysis of question-answering steps in advance during knowledge graph construction. By completing this detailed segmentation and tagging of knowledge points and questioning styles beforehand, the system avoids repeating this computationally intensive work during actual question generation, thus reducing real-time processing time while maintaining high precision
Data Source
AI summary
The disclosure provides an information processing method and device. In one embodiment, an information processing method comprises receiving a request for generating questions inputted by a user, the request for generating questions includes a to-be-learned knowledge point; acquiring, from a knowledge graph for questions, a node path including a target node indicating the to-be-learned knowledge point, the nodes in the knowledge graph for questions indicating question-answering steps of existing questions, knowledge points tested in the question-answering steps, and questioning styles corresponding to the question-answering steps; and generating questions required by the user according to question-answering steps, knowledge points tested in the question-answering steps, and questioning styles corresponding to the question-answering steps indicated by nodes on the node path. The present disclosure enables generation of new questions and facilitates the expansion of a question bank.