Hyper Relational Knowledge Graph

Hyper-relational knowledge graphs (HKGs) enhance traditional knowledge graphs by incorporating rich contextual information, or "qualifiers," alongside core relationships, enabling more nuanced and accurate representation of complex facts. Current research focuses on developing effective embedding methods, often employing graph neural networks and transformers, to capture the intricate relationships within HKGs and improve performance on tasks like link prediction, query answering, and knowledge graph completion. This work is significant because HKGs offer a more expressive framework for representing real-world knowledge, leading to improved performance in various applications, including question answering systems and recommendation systems.