Automatic Question Generation with Knowledge Graph for Panoramic Learning

In recent years, the global social landscape has become increasingly complex, requiring the ability to think from a wide range of diverse perspectives for effective problem-solving. In the field of education, panoramic learning, which implements interdisciplinary and comprehensive education, has become essential. Also, there has been recent research on various aspects of automatic question generation (AGQ), with some studies focusing on generating panoramic questions, which provide a comprehensive understanding, across different genres using knowledge graph (KG). KG is a knowledge base that uses a graph-structured data model and consists of entities and relationships between entities. On the other hand, research on generating panoramic questions for specific subjects with educational purposes has been limited, and this study aims to address that. In this work, we specifically targeted the field of history for question generation and used complemented entities to enhance the inclusion of panoramic knowledge in the field of history. The approach involves enhancing subgraphs with link prediction, which complements missing relationships in KGs, particularly in historical contexts requiring temporal and spatial insights. Through evaluation, it was validated that the proposed method could generate questions containing more panoramic knowledge compared to existing methods.