Multimodal Knowledge Graph

Multimodal knowledge graphs (MMKGs) integrate information from diverse sources like text, images, and other modalities, aiming to improve knowledge representation and reasoning capabilities beyond single-modality approaches. Current research focuses on developing efficient methods for MMKG construction, leveraging techniques like optimal transport for correlation assignment and transformer-based architectures for multimodal fusion and reasoning, often incorporating knowledge graph structure into the model. This field is significant because MMKGs enhance the accuracy and efficiency of various applications, including question answering, entity linking, and zero-shot learning, particularly in domains with rich multimodal data like biomedicine and e-commerce.