Knowledge Representation

Knowledge representation is the old AI discipline MDDE quietly rests on: expressing what you know — concepts, relationships, rules, constraints — in a structured, typed form that a machine can act on, not just display. A wiki page about "customer churn" is knowledge for humans; a modeled concept with typed relationships and executable rules is knowledge for the platform. This is why the form of metadata matters so much in MDDE. Free-text descriptions cannot drive generation; structured, typed metadata can. Models, an enterprise ontology, mapping specifications, and rule definitions are all knowledge representations — and the richer and more disciplined the representation, the more the platform can derive from it deterministically and the more safely AI can reason over it via a reasoning layer. The concept also explains MDDE's relevance in the AI era: large language models are strong at language and weak at ground truth. A platform whose knowledge is represented as structured metadata gives AI something solid to reason against — the difference between an assistant that guesses at your definitions and one that reads them.