fix(docs): fix broken relative links in capture-knowledge objective

Author: jgeluk

docs/objective/capture-knowledge.md

@@ -1,38 +1,74 @@
 ---
-title: >-
-  D: Capture Knowledge - Enterprise Knowledge Mapping
-authors:
-  - Jacobus Geluk
-hide:
-  - toc
-date_published: "2022-01-01"
-letter_prefix: "D"
 description: >-
-  Learn how to enable a whole new league of use cases through
-  comprehensive knowledge capture. Discover how the Use Case Tree
-  Method maps all knowledge, data, and functionality.
+  Learn how to capture and preserve institutional knowledge using
+  Enterprise Knowledge Graphs. Discover how EKG serves as the
+  ground-truth foundation for AI acceleration and SME productivity.
 keywords:
   - knowledge capture
-  - knowledge management
-  - enterprise knowledge
+  - institutional knowledge
+  - subject matter expert
+  - SME productivity
+  - AI acceleration
+  - GraphRAG
   - EKG method
   - enterprise knowledge graph
-  - knowledge mapping
 schema_type: "Article"
+letter_prefix: "D"
 ---
 
 # Capture Knowledge
 
 <!--summary-start-->
 
-_Enabling a whole new league of use cases._
+_Preserving institutional expertise to enable a whole new league of AI-driven use cases._
 
 <!--summary-end-->
 
-The [Use Case Tree](../concept/use-case-tree.md) **Provides a
-"map"[^1] of all knowledge, data and functionality** that the EKG
-provides to the enterprise. Enabling a whole new league of use cases.
+In every enterprise, the most valuable asset is often the most fragile: **Institutional Knowledge**. This knowledge exists in two forms: **Explicit Knowledge** (scattered across PDFs, diagrams, and emails) and **Tacit Knowledge** (the specialized "know-how," rules of thumb, and method choices residing in the heads of your Subject Matter Experts (SMEs)).
+
+The Use Case Tree Method uses the Enterprise Knowledge Graph (EKG) as the definitive technology stack to capture, formalize, and activate this knowledge before it "walks out the door."
+
+## The Institutional Knowledge Problem
+
+Traditionally, when an SME leaves or retires, their expertise leaves with them. Documentation is often a "write-once, read-never" graveyard of information that AI cannot reliably use without context.
+
+This creates a bottleneck:
+- **Productivity Drain**: SMEs spend 30-50% of their time answering the same questions or hunting for data.
+- **AI Hallucinations**: Generic AI models lack the "ground truth" of your specific business logic, leading to unreliable outputs.
+- **Lost Rationale**: We know *what* was decided in the past, but we've lost the *why*—the expert logic that drove those decisions.
+
+## How EKG Captures Expertise
+
+An EKG doesn't just store data; it captures **meaning and relationships**. By using the [Use Case Tree](../concept/use-case-tree.md), organizations can systematically extract knowledge from SMEs and turn it into **executable models**.
+
+1. **Structured SME Logic**: SMEs define the [Concepts](../concept/concept.md), [Outcomes](../concept/outcome.md), and [Workflows](../concept/workflow.md) that drive the business. This transforms "head-knowledge" into machine-readable [Ontologies](../concept/ontology.md).
+2. **Contextual Mapping**: Every data point is linked to the business capability it supports. You aren't just capturing "what" the data is, but the **constraints and dependencies** that define its use.
+3. **Durable Intellectual Property**: The knowledge becomes part of the enterprise's software fabric—reusable, versioned, and protected from employee turnover.
+
+## AI Acceleration: The Ground Truth Foundation
+
+The rise of Large Language Models (LLMs) has changed the game. AI is no longer just a tool; it is a productivity accelerator. However, AI is only as good as the knowledge it is grounded in.
+
+**EKG is the "Fact-Checking Guardrail" for AI.**
+
+- **GraphRAG (Retrieval-Augmented Generation)**: Instead of letting an AI guess, you feed it structured facts from your EKG. This ensures the AI's output is accurate, traceable, and compliant with enterprise standards.
+- **Neuro-symbolic Harmony**: We combine the **probabilistic power** of GenAI (extracting knowledge from millions of documents) with the **symbolic precision** of EKGs (the validated rules provided by your SMEs).
+- **Accelerating SMEs**: Instead of doing the "grunt work" of data retrieval, your experts use AI to turn interviews and past debriefs into structured knowledge linked to evidence, which is then instantly validated against the EKG.
+
+!!! success "The Result: A New League of Use Cases"
+    By capturing knowledge in an EKG, you move from "Data Management" to "Intelligence Management." You enable strategic use cases—like [Complex Construction Quotations](strategic-usecases.md#complex-construction-project-quotations)—that were previously impossible due to the sheer volume of expert knowledge required.
+
+## Why it Matters Now
+
+As AI begins to take over routine cognitive tasks, the primary role of the human expert shifts from **doing the task** to **defining the logic** that drives the AI. 
+
+Capturing knowledge in an EKG ensures that your organization owns the "brain" of its AI systems. It isn't just about efficiency; it's about **Agility**—the ability to reuse expert knowledge across the entire organization to build new capabilities at the speed of thought.
+
+---
+
+### Related Content
 
-[^1]:
-    See also
-    [Align with Business Strategy: Business Capability Maps](align-with-business-strategy.md)
+- **[Align with Business Strategy](align-with-business-strategy.md)** - Mapping knowledge to goals
+- **[Strategic Use Cases](strategic-usecases.md)** - Where captured knowledge delivers the most value
+- **[Use Case Tree](../concept/use-case-tree.md)** - The structure for mapping all knowledge
+- **[Modularity Managed](modularity.md)** - Turning knowledge into reusable components