Why Knowledge Graphs and Ontologies Are Making a Comeback in the Age of AI
Knowing what your data means is the foundation. Getting people and agents to act on that meaning, and proving that it was allowed, is building on the foundation. Context runs the spectrum of semantics, state, and controls.
When Forrester and Gartner publish competing context layer definitions in the same quarter, the word is mainstream. When Google pays $10 million for a dead airline’s internal business data, the value is mainstream. When W3C and Google publish competing formats for machine-readable wikis, the plumbing is mainstream.
Mainstream doesn’t mean settled, but whichever way you cut it, semantics is a key part of context. The need to define semantics is what led to the resurgence of ontology. Two things are true about it at once now.
Ontology is more necessary than ever; it’s the guardrail, not the prompt. It’s what your data means, and makes a system’s assumptions explicit and machine-processable. The hard...
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