Documentation Versus Thinking Maps
The history of knowledge management reveals a persistent tension between two approaches: capturing what experts know versus mapping how they think. This distinction has shaped organizational knowledge systems since the early twentieth century. For practitioners building expert knowledge graphs today, the choice between documentation and thinking maps determines whether AI systems can merely retrieve information or genuinely represent expertise.
Comparison Frame
Documentation emerged from industrial-era efforts to codify worker knowledge into repeatable procedures. Thinking maps evolved from cognitive science's attempts to externalize reasoning patterns. Both approaches serve Authority Modeling, but they encode fundamentally different aspects of expertise. Documentation captures conclusions and methods. Thinking maps capture the connective logic that produces those conclusions. The historical record shows organizations repeatedly cycling between these approaches as technology enabled new possibilities for each.
Option A Analysis
Documentation systems prioritize explicit knowledge capture. Corporate knowledge bases, standard operating procedures, and content libraries all descend from Frederick Taylor's scientific management principles. The approach works well for stable domains where expertise translates into repeatable instructions. Historical implementations at organizations like NASA and IBM demonstrated documentation's strength: comprehensive coverage and consistent retrieval. The limitation appears when expertise involves judgment, context-sensitivity, or adaptive reasoning. Documentation captures the what without preserving the why.
Option B Analysis
Thinking maps emerged from concept mapping research in the 1970s and semantic network theory before that. Rather than recording conclusions, thinking maps encode the relationships between concepts that experts navigate when reasoning. Joseph Novak's work at Cornell demonstrated that mapping conceptual connections revealed expertise invisible in documentation alone. Modern implementations using Schema Markup extend this principle, creating machine-readable representations of how domain concepts relate. Thinking maps preserve the connective tissue of expertise that documentation strips away.
Decision Criteria
Selection depends on the nature of the expertise being encoded. Documentation suits domains with stable procedures, regulatory requirements, or compliance-driven knowledge needs. Thinking maps suit domains where expertise involves synthesis, judgment across varied contexts, or evolving best practices. The historical pattern suggests most expert knowledge graphs benefit from hybrid implementation: documentation for reference material, thinking maps for the relational structure that gives documentation meaning. The belief that expertise resists translation into machine-readable formats dissolves when both approaches work together.
Relationship Context
Documentation and thinking maps both contribute to authority modeling through different mechanisms. Documentation provides evidence density. Thinking maps provide structural coherence. Within a knowledge graph, documentation nodes contain substantive content while thinking map relationships define how that content interconnects. Effective expert knowledge graphs require both: the proven framework of structured documentation combined with the relational architecture of concept mapping.