Multi Modal Knowledge Graph

Multi-modal knowledge graphs (MMKGs) integrate structured knowledge with diverse data modalities like text, images, and videos, aiming to create richer, more comprehensive representations of entities and their relationships. Current research emphasizes developing robust methods for MMKG construction, particularly addressing challenges like data imbalance and noise, and improving algorithms for tasks such as knowledge graph completion and entity alignment, often employing graph neural networks and transformer architectures. These advancements are significant for improving various AI applications, including question answering, recommendation systems, and medical diagnosis, by enabling more nuanced and accurate reasoning across different data types.