Concepts
The irreducible epistemic atoms underlying the curriculum. 4,828 atoms across 8 types
Use typed relationships with semantic labels rather…
Use typed relationships with semantic labels rather than generic connections to reduce ambiguity and enable automated reasoning over knowledge structures.
Limit relationship type taxonomies to 5-7 categories…
Limit relationship type taxonomies to 5-7 categories that match actual cognitive distinctions rather than pursuing ontological completeness.
Force relationship classification at link creation time…
Force relationship classification at link creation time to detect spurious connections before they accumulate as noise in the system.
Measure knowledge depth by calculating edge-to-node ratios…
Measure knowledge depth by calculating edge-to-node ratios within conceptual clusters rather than counting individual facts or node quantities.
Build conceptual triangles by explicitly connecting nodes…
Build conceptual triangles by explicitly connecting nodes that share a common parent, converting sparse star topologies into dense mesh structures that support multiple retrieval paths.
Conduct periodic orphan audits with rapid binary…
Conduct periodic orphan audits with rapid binary triage—connect, delete, or incubate for 30 days—treating connection speed as a system health metric.
Invest maintenance effort proportional to node connectivity…
Invest maintenance effort proportional to node connectivity rather than treating all notes equally, concentrating review cycles and revision work on high-degree nodes that hold graph structure.
Distinguish organic hubs that earn centrality through…
Distinguish organic hubs that earn centrality through genuine conceptual relationships from artificial index hubs that create cosmetic connectivity without semantic weight.
Identify structural gaps in your knowledge by detecting…
Identify structural gaps in your knowledge by detecting clusters that should be connected but have no bridge nodes, revealing blind spots where cross-domain synthesis is missing.
Test bridge node validity by generating novel predictions…
Test bridge node validity by generating novel predictions or insights in both connected domains—if the connection produces only similarity recognition without actionable transfer, demote it to metaphor status.
Build knowledge graphs with T-shaped topology—deep clusters…
Build knowledge graphs with T-shaped topology—deep clusters connected by bridge nodes—rather than uniform density or disconnected islands, optimizing for both depth and strategic breadth.
Use spatial visualization to reveal structural properties…
Use spatial visualization to reveal structural properties of your knowledge graph that sequential text-based interfaces necessarily hide.
Trace the shortest path between two seemingly unrelated…
Trace the shortest path between two seemingly unrelated concepts to reveal the hidden chain of relationships connecting them.
Discover your actual domains of expertise by identifying…
Discover your actual domains of expertise by identifying naturally formed clusters of densely interconnected concepts rather than imposing predetermined categories.
Trust the structural patterns in your knowledge graph over…
Trust the structural patterns in your knowledge graph over your self-narrative about what you know, because graph structure reflects actual cognitive investment while self-assessment is systematically distorted.
Measure domain depth by examining both the size of concept…
Measure domain depth by examining both the size of concept clusters and the density of connections within them, where high density indicates structural comprehension beyond mere fact accumulation.
Diagnose specific knowledge deficiencies by identifying…
Diagnose specific knowledge deficiencies by identifying where edges should exist between concepts you already have but have not articulated the relationship.
Distinguish between missing edges (concepts…
Distinguish between missing edges (concepts you have but haven't connected), sparse nodes (concepts with insufficient connections), and missing nodes (concepts absent from your graph) because each requires different remediation strategies.
Prioritize filling knowledge gaps that are adjacent…
Prioritize filling knowledge gaps that are adjacent to your densest clusters because these represent concepts within your zone of proximal development where learning will be most effective.
Integrate new knowledge immediately through connecting…
Integrate new knowledge immediately through connecting it to existing nodes rather than accumulating unprocessed material, because the integration act forces retrieval and spaced repetition of surrounding concepts.
Design external systems to survive the death of the tools…
Design external systems to survive the death of the tools that currently access them by storing knowledge in durable, open, human-readable formats with explicit connections.
Conduct regular structural reviews of your knowledge graph…
Conduct regular structural reviews of your knowledge graph to detect and repair decay (dead links, missing connections, orphan nodes) before trust degradation makes the system unusable.
Start graph exploration with overview (shape), then zoom…
Start graph exploration with overview (shape), then zoom to filtered regions (relationships), then examine specific nodes (details), rather than beginning with targeted search.
Couple AI systems with explicitly structured personal…
Couple AI systems with explicitly structured personal knowledge graphs rather than unlinked text collections to enable graph traversal and structural reasoning beyond semantic similarity.