Knowledge graphs are moving from niche to mainstream. Early adopters who embrace ontologies and semantic layers are already seeing measurable business impact. Here’s guidance for building your own successful knowledge graph.

From SAP and Netflix to ServiceNow and Samsung, leading organisations are embracing knowledge graphs and ontologies as foundational infrastructure for AI and analytics. The reason is clear: AI needs context, businesses need unified meaning, and users need semantic access to insights. The knowledge graph is becoming the new enterprise standard.

Every graph professional eventually meets the Hairball of Hell — the tangled mess that emerges when beautiful graphs outgrow human perception. While AI can thrive in this complexity, we can’t. The solution isn’t more visualisation; it’s meaning. Ontology acts as a semantic scaffold, helping both humans and machines navigate complexity with purpose.

Ontologies aren’t just knowledge maps — they’re mathematical compressions. By defining meaningful classes and relationships, you’re factorising high-dimensional data into a lower-dimensional conceptual space. This selective abstraction turns raw information into structured insight, giving AI systems clarity and focus.