Automatic Question Generation Based on Historical Panoramic Knowledge Graphs and Inference Rules

There has been a lot of research on using data from Wikipedia and other sources as a knowledge graph to generate questions for learning history and other subjects. These knowledge graphs consist of entities (words) and relations (links) between the entities, and the existing methods generated questions by extracting small subgraphs from the knowledge graphs and hiding target words (correct answer words). However, questions generated by existing methods can be solved with narrow knowledge, so they do not contribute to the development of panoramic ability that has been increasingly demanded in recent years. While increasing the size of the extracted subgraph enhances the panoramic of the question, if the subgraph is too large, it becomes difficult to understand and time-consuming to learn. Therefore, in this paper, our goal is to enhance the panoramic while keeping the subgraph small. Specifically, we prioritize extracting entities within the subgraph that are semantically distant from the correct answer word. Furthermore, we propose a method to add bypass links based on the inference rules to ensure that the extracted entities are connected to the correct answer word with minimal hops from the perspective of temporal and spatial panoramic knowledge. Since KGs based on Wikipedia do not represent all common knowledge, we utilize inference rules to complement the correct relations without contradictions. As a result of conducting subjective evaluation experiments with participants and objective evaluation experiments about the traversal of temporal and spatial knowledge from history subjects, it was confirmed that the proposed method can generate more panoramic and comprehensive questions in both temporal and spatial dimensions, at a similar scale to existing methods. © 2025, Japanese Society for Artificial Intelligence. All rights reserved.