Heterogeneous Knowledge Graph

Heterogeneous knowledge graphs (HKGs), which integrate diverse data types and relationships, are increasingly used to address challenges in knowledge representation and reasoning. Current research focuses on developing and improving methods for knowledge graph completion, leveraging architectures like graph attention networks and graph convolutional networks to predict missing links and enhance knowledge representation. These advancements are impacting various fields, including improved student performance prediction in online learning, more effective question answering systems, and enhanced medical diagnosis prediction through the integration of external medical knowledge. The ability to effectively analyze and utilize the complex information within HKGs is driving progress in numerous data-intensive applications.