knowledge-modeling
Knowledge Modeling
Coverage
The representation-strategy layer above conceptual modeling and below formal ontology. Names seven knowledge-representation paradigms — Knowledge Graph, Frames, Semantic Network, Production Rules, Concept Map, Process / Procedural Ontology (PKO), Hybrid — with structure, best-for, and weakness for each. Specifies the tacit-to-explicit knowledge acquisition pipeline that converts what experts know-but-cannot-articulate into computable form (elicitation → articulation → formalization → expert validation → encoding) and the five knowledge sources (domain experts, documentation, existing code, user behaviour, failure post-mortems). Lays out knowledge-graph design principles: reify when a relationship has properties, separate schema-level from instance-level, label edges precisely (created_by not related_to), enforce bidirectional naming, minimise redundancy. Covers the four validation types (completeness / consistency / relevance / currency) plus expert walkthrough. Walks the seven-phase knowledge lifecycle (Create → Validate → Publish → Use → Monitor → Update → Retire) with each phase's failure mode. Maps the theory to AI-agent systems: skills as frames, routing as production rules, memory as a knowledge graph with temporal properties. Devotes a full section to GraphRAG with five concrete patterns (entity-anchored retrieval, relationship-aware context, path-based reasoning, subgraph summarization, hybrid vector + graph) and the rules for when graph-grounded retrieval actually beats plain vector RAG. Closes with the representation-tradeoff matrix between expressiveness and tractability across reasoning, querying, maintenance, and human readability.
Philosophy
Knowledge is not data. Data records facts; knowledge encodes the judgment and context needed to act on those facts. A database stores that an order has status refunded. Knowledge captures that a refund after thirty days requires manager approval, that the customer's lifetime value should influence the response, and that the upstream fulfilment pipeline has a known forty-eight-hour delay. The agent that has only the data hallucinates the policy; the agent that has the knowledge applies it.
AI-agent systems are knowledge systems in disguise. Every SKILL.md is a knowledge artefact. Every reference doc is a knowledge artefact. Every routing rule, every memory file, every decision record is a knowledge artefact. The question is not whether the workspace has a knowledge model — it always does, even when implicit — but whether the model fits the dominant query pattern. Multi-hop reasoning needs a graph. Decision logic needs production rules. Object-like domain entities need frames. "Why did the agent decide X?" needs a procedural ontology. Pick the wrong paradigm and the agent's reasoning breaks against the representation rather than against the domain.
The representation-vs-reasoning tradeoff is non-negotiable. More expressive representations (OWL-DL, full first-order logic) admit fewer fast queries; more tractable representations (property graphs, key-value) admit fewer formal proofs. For most product teams the right answer is "Markdown with conventions" — which is what a SKILL.md is — not formal ontology. Escalate formality only when automated reasoning or multi-system interop demands it.