The effective modeling and querying of the world’s ever-growing collection of multimedia is a significant challenge. Multimodal knowledge graphs offer a powerful solution by integrating heterogeneous data sources and uncovering complex relationships. We point to its benefits across a range of applications, including holistic memory retrieval, interactive video exploration, cross-modal fact-checking for journalism, and data fusion for clinical diagnosis. We also explore its use in enhancing educational tools, product feedback analysis in e-commerce, and semantic annotation of textual corpora. By showcasing how these diverse applications model multimedia at its core, we illustrate the potential of a multimodal knowledge graph store that can directly leverage and process the content of multimedia. However, a major limitation of existing approaches is that they treat multimedia documents as opaque entities, which severely limits their analytical potential. In this paper, we introduce MeGraS, our novel multimodal knowledge graph store that embodies a new paradigm: to provide direct access to multimedia document content, enabling deeper and more flexible analysis.