Knowledge Graphs in Principle and Practice: Neural KGs and the Landscape of Meaning [3/8]

Module III: Neural Knowledge GraphsTransitioning from "Hard Logic" to "Soft/Statistical Reasoning," this module covers geometric deep learning on graphs.Knowledge Graph Embeddings (KGE):Translational Models: TransE (h+r≈t) and its limitations.Rotational Models: RotatE, which uses rotations in a complex plane to model symmetry and inversion.Graph Neural Networks (GNNs):Neural Message Passing: The iterative process of aggregating neighbor information to update node features.Relational Architectures: R-GCNs and Graph Attention Networks (GATs).Theoretical Limits: Understanding the Weisfeiler-Lehman (WL) Limit and why standard GNNs may fail to distinguish certain graph topologies.