Multimodal Knowledge Graph Construction

Multimodal knowledge graph construction (MKGC) aims to build comprehensive knowledge graphs by integrating information from diverse data sources like text and images. Current research focuses on addressing challenges like continual learning (handling dynamically changing data) and improving the joint extraction of entities and relations from multimodal data, often employing graph neural networks and novel alignment techniques to effectively fuse information across modalities. These advancements are crucial for improving the performance of embodied AI systems and other applications requiring robust knowledge representation from complex, real-world data.