Scalable Uncertainty Reasoning in Knowledge Graphs

This research proposes a modular framework to address scalable uncertainty reasoning in Knowledge Graphs, where real-world data often inherently contains uncertainty. It tackles three levels of uncertainty—imprecise attributes, probabilistic triple existence, and incomplete schema knowledge—through tailored techniques like probabilistic literals, probabilistic circuits, and geometric embeddings.