# Knowledge Graphs for Industrial Data Applying [[knowledge graphs]] to industrial operations means mapping the relationships between physical assets, sensors, processes, and operating conditions into a queryable semantic structure. A refinery has 50,000+ sensors. Raw data is meaningless without context: which sensor is on which piece of equipment, what process it monitors, what the normal operating range is, what happens upstream and downstream. A knowledge graph encodes all of this context. Key players: OSIsoft (now AVEVA), AWS SiteWise, Azure Digital Twins, Honeywell. All offer industrial knowledge graph capabilities. This is standard infrastructure, not a differentiator. The potentially novel application: using the knowledge graph to auto-generate physics models. If you can automatically map sensor relationships to first-principles equations, you eliminate the most expensive step in building [[Digital Twins]]. This is the "KG-to-model automation pipeline" that is the least evidenced but most important capability in industrial AI. Think of it as the Palantir analogy for physical systems. Palantir's moat is not any single algorithm. It's the integration layer that connects messy real-world data to actionable intelligence. Same pattern applies here. Related: [[knowledge graphs]], [[Digital Twins]], [[Auto-Generated Physics Models]], [[Industrial AI MOC]] --- Tags: #deeptech