The Spatial Web as a Knowledge Graph: Modeling Reality, Behavior, and Trust
36m
https://drive.google.com/file/d/1047w3_Vfg6FGK8HdLFp95NgjucDsxlzO/view?usp=share_link
The Spatial Web, as defined by the IEEE P2874 standard, extends the World Wide Web and traditional knowledge graphs beyond static facts to include entities, spaces, behaviors, and governance spanning the physical and digital world. As AI agents, automation, and cyber-physical systems increasingly rely on knowledge graphs for real-world decision-making, existing graph models struggle to represent behavior, policy, trust, and spatial context in a unified, machine-interpretable, and explainable way. In most systems, actions, rules, validation, and enforcement remain embedded in application code rather than modeled explicitly in the graph.
This presentation introduces HSML (Hyperspace Modeling Language), a semantic modeling framework developed within IEEE P2874 to extend knowledge graphs with first-class representations of entities, spaces, activities, agents, and governance constraints. HSML enables knowledge graphs to capture not only structure and meaning, but also behavior, action invocation, and authority across organizational and technical boundaries.
HSML is grounded in a hyperspace paradigm that integrates spatial, temporal, semantic, organizational, and operational dimensions into a coherent graph model. Its spatial and identity foundations align with decentralized web standards, including Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), enabling interoperable and governed knowledge graphs for AI, digital twins, and agentic systems.