Multimodal KGs

Multimodal knowledge graphs represent entities and their relationships alongside evidence such as text, images, video, and timestamps, enabling reasoning across different kinds of data. Representation learning methods—including graph embeddings, contrastive alignment, and graph neural networks—support tasks such as link prediction, entity alignment, and completion, while graph construction and retrieval-augmented generation connect these methods to question answering and domain applications. Evaluation spans link-prediction benchmarks and multimodal question-answering settings in areas such as biomedicine, e-commerce, and personal data. A central challenge is making retrieved evidence reliable when modalities are missing, noisy, or ambiguous, and measuring whether retrieval quality actually improves grounded reasoning.