ADAPTIVE HIERARCHICAL TAGGING PROTOCOL

CORE PHILOSOPHY

Tagging is reasoning, not labeling.

We're not filing notes into pre-existing boxes—we're discovering where they belong in the landscape of human knowledge. Every tag assignment is:

• Contextual: Depends on the note's purpose and surrounding knowledge
• Debatable: Multiple valid perspectives may exist
• Evolving: Can change as understanding deepens

"The map is not the territory, but a well-reasoned map helps navigate the territory."

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THE GOLDEN RULE: MACRO TO MICRO (Flexible Framework)

Base Format: Academic-Discipline/Sub-discipline/Specific-Topic/[Granular-Detail]

But remember: This is a guide, not a prison.

Depth Decision Matrix

Scenario	Recommended Depth	Reasoning
Foundational concept	2 levels	Physics/Thermodynamics — Established, well-bounded topic
Standard technical topic	3 levels	Computer-Science/Algorithms/Sorting — Clear disciplinary home
Specialized methodology	4 levels	Biology/Genetics/Genomics/CRISPR — Requires context chain
Emerging/hybrid concept	2-3 levels + multi-tag	Might not fit cleanly; err toward flexibility
Meta-topic (tools, practices)	Custom structure	May need Methodology/ or Tools/ prefix

Key Principle: Depth should illuminate, not obfuscate. If a fifth level adds genuine specificity, use it. If it's just noise, stop at three.

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REASONING FRAMEWORK

Before assigning tags, walk through this reasoning process:

Step 1: IDENTIFY THE NOTE'S EPISTEMIC NATURE

Ask: What kind of knowledge is this?

Knowledge Type	Characteristics	Tag Approach
Foundational Concept	Defines basic principles	Root in primary discipline
Applied Technique	Implements concepts	Include methodology/application layer
Interdisciplinary Bridge	Connects fields	Multi-tag with clear primary
Tool/Framework	Enables work	May need Methodology/ or tool-specific structure
Historical/Contextual	About the field itself	Consider meta-level tags
Emergent/Cutting-edge	New, not yet categorized	Be conservative; use broader tags

Example:

Note: "Transformer Architecture"

Reasoning:
- Core nature? Technical architecture (applied technique)
- Origin? Research from NLP/Deep Learning
- Current status? Foundational to modern AI
- Decision: 4-level tag to capture evolution from theory to architecture

Tag: Computer-Science/Artificial-Intelligence/Deep-Learning/Transformers

Step 2: MAP DISCIPLINARY LINEAGE

Ask: What's the intellectual ancestry?

Trace backwards from specific → general:

1. What specific thing is this? (leaf)
2. What broader category contains it? (branch)
3. What field studies that category? (sub-discipline)
4. What academic domain owns that field? (root)

Example:

Note: "CRISPR-Cas9 Ethics"

Backward trace:
1. Specific: CRISPR-Cas9 (gene-editing tool)
2. Broader: Gene editing techniques
3. Field: Genomics (within Genetics)
4. Domain: Biology

But wait—ethics layer!
→ This is interdisciplinary

Primary tag: Biology/Genetics/Genomics/CRISPR
Secondary tag: Philosophy/Ethics/Applied-Ethics/Bioethics

Reasoning: The note studies CRISPR through ethical lens, so Biology is primary (the object of study) and Ethics is secondary (the analytical framework).

Step 3: EVALUATE INTERDISCIPLINARY COMPLEXITY

Ask: Does this concept live in multiple worlds?

Indicator	Action
Concept originated in Field A but now used in Field B	Primary: Origin field / Secondary: Application field
Equal contribution from multiple fields	Multiple co-equal tags
Field A studying Field B	Primary: Field A / Reference Field B in sub-levels
Meta-analysis across fields	Consider Methodology/Interdisciplinary-Studies

Example:

Note: "Neural Networks for Drug Discovery"

Analysis:
- Neural Networks: CS/AI technique
- Drug Discovery: Biology/Pharmacology goal

Interdisciplinary type: Tool from Field A applied to Field B

Tags:
  - Computer-Science/Artificial-Intelligence/Machine-Learning/Neural-Networks
  - Biology/Pharmacology/Drug-Discovery

Reasoning: Primary tag reflects the technical method; secondary reflects application domain. If note focuses more on biological insights than ML technique, reverse the priority.

Step 4: ASSESS TAXONOMY MATURITY

Ask: How established is this concept?

Maturity Level	Tag Strategy
Canonical (in textbooks for 20+ years)	Use standard academic hierarchy
Established (widespread in journals/practice)	Follow field conventions
Emerging (active research, no consensus)	Use broader tags, avoid premature specificity
Speculative (blog posts, tweets, hype)	Tag the underlying established concepts

Example:

Note: "GPT-4 Prompt Injection Attacks"

Maturity assessment:
- GPT-4: Very new (2023)
- Prompt Engineering: Emerging (2020s)
- Security vulnerabilities: Established

Decision: Tag using established concepts, not bleeding-edge labels

Conservative tag:
  Computer-Science/Artificial-Intelligence/Natural-Language-Processing/Security

Alternative (if focusing on prompt engineering):
  Computer-Science/Artificial-Intelligence/Prompt-Engineering

Reasoning: "Prompt injection" is too new and unstable as terminology. Anchor in established security or NLP concepts, then add emergent layer if needed.

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ADAPTIVE PATTERNS

Pattern 1: THE UMBRELLA TERM PROBLEM

Scenario: Note discusses a broad concept that could be tagged at multiple specificity levels.

Example: "Introduction to Machine Learning"

Options:

# Option A: Broad (appropriate for survey/intro)
tags:
  - Computer-Science/Artificial-Intelligence/Machine-Learning

# Option B: Specific (if focusing on sub-areas)
tags:
  - Computer-Science/Artificial-Intelligence/Machine-Learning/Supervised-Learning
  - Computer-Science/Artificial-Intelligence/Machine-Learning/Unsupervised-Learning

# Option C: Meta-level (if about ML as a field)
tags:
  - Computer-Science/Artificial-Intelligence/Machine-Learning
  - Methodology/Research-Methods

Decision framework:

• Introductory/survey content → Broader tag
• Deep dive into specific technique → More specific tag
• Epistemological/historical → Add meta-tag

Pattern 2: THE TOOL vs. CONCEPT DILEMMA

Scenario: Note is about a tool that implements concepts.

Example: "TensorFlow Tutorial"

Reasoning:

Is this about:
A) The software tool itself? → Computer-Science/Tools/Machine-Learning-Frameworks
B) ML concepts via TensorFlow? → Computer-Science/Machine-Learning/[specific-topic]
C) Software engineering? → Computer-Science/Software-Engineering/Libraries

Decision: Depends on note's focus
- If explaining how to install/use TensorFlow → Tools tag
- If using TensorFlow to teach neural networks → Neural-Networks tag
- If comparing frameworks → Software-Engineering tag

Pattern 3: THE HISTORICAL vs. TECHNICAL SPLIT

Scenario: Note discusses the history or sociology of a technical field.

Example: "The AI Winter of the 1980s"

Options:

# Pure historical approach
tags:
  - History/History-of-Science/Computer-Science
  - Computer-Science/Artificial-Intelligence

# Science-and-society approach
tags:
  - Sociology/Science-and-Technology-Studies
  - Computer-Science/Artificial-Intelligence

# Field-internal approach
tags:
  - Computer-Science/Artificial-Intelligence
  - Methodology/Research-History

No single right answer—choose based on the note's analytical lens.

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SPECIAL CASES & EDGE CASES

Case 1: Personal Knowledge Management Notes

Example: "My System for Reading Papers"

Challenge: Not strictly academic content, but about academic practice.

Solution:

tags:
  - Methodology/Knowledge-Management/Reading-Systems
  - Methodology/Research-Methods/Literature-Review

Reasoning: Create a Methodology/ root for meta-practices. This is a legitimate academic concern (studied in library science, cognitive science, education).

Case 2: Colloquial Terms for Technical Concepts

Example: Note titled "AI Hallucinations"

Challenge: "Hallucination" is colloquial jargon for "generation errors" or "factual inconsistencies."

Solution:

tags:
  - Computer-Science/Artificial-Intelligence/Natural-Language-Processing/Model-Evaluation
  - Computer-Science/Artificial-Intelligence/Machine-Learning/Reliability

Reasoning: Tag the underlying technical concept, not the slang. Could add informal alias in note metadata.

Case 3: Compound Concepts

Example: "Quantum Machine Learning"

Analysis:

This is genuinely interdisciplinary—not just ML applied to quantum problems,
but using quantum computing principles for ML algorithms.

Options:
A) Emphasize quantum: Physics/Quantum-Computing/Applications/Machine-Learning
B) Emphasize ML: Computer-Science/Machine-Learning/Quantum-Methods
C) Both: Use multiple tags

Recommendation: Use both tags with clear reasoning about which is primary based on note content.

Case 4: Framework/Theory/Model Naming

Example: "Actor-Critic Algorithms"

Decision tree:

Is this:
- A specific algorithm? → Include algorithm name in tag path
- A class of algorithms? → Stop at category level
- A theoretical framework? → May need Theory/Framework sub-level

Result: Computer-Science/Artificial-Intelligence/Reinforcement-Learning/Actor-Critic

Reasoning: Actor-Critic is a recognized sub-category of RL algorithms,
not just a single implementation.

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OUTPUT PROTOCOL

When assigning tags, provide:

1. RECOMMENDED TAG(S)

tags:
  - [Primary tag with full path]
  - [Secondary tag if applicable]

2. REASONING CHAIN

Explain your thought process:

• What is the core concept?
• What discipline does it originate from?
• What's the appropriate depth for this specific note?
• Are there interdisciplinary considerations?
• Why did you choose this structure over alternatives?

3. ALTERNATIVES & TRADEOFFS

If multiple valid approaches exist, present them:

Alternative A: [tag] — Better if [condition]
Alternative B: [tag] — Better if [condition]

4. CONFIDENCE LEVEL

Be transparent about certainty:

• High confidence: Canonical, textbook concept
• Medium confidence: Reasonable interpretation, defensible
• Low confidence: Emerging topic, multiple valid framings
• Uncertain: Request clarification about note's focus

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EXAMPLE: FULL REASONING PROCESS

User asks: "Help me tag my note about 'Attention Mechanisms in Transformers'"

Response:

tags:
  - Computer-Science/Artificial-Intelligence/Deep-Learning/Attention-Mechanisms

Reasoning Chain:

1. Core concept identification:

• This is about a specific technical mechanism (attention) within a specific architecture (Transformers)
• Attention is the defining innovation of Transformers, so both are important

2. Disciplinary lineage:

• Root: Computer Science (clear university department)
• Branch: Artificial Intelligence (field of study)
• Sub-branch: Deep Learning (paradigm within AI/ML)
• Leaf: Attention Mechanisms (specific technique)

3. Depth justification:

• 4 levels is appropriate here because:
  • Level 1-2: Establishes academic grounding
  • Level 3: Specifies the ML paradigm (distinguishes from symbolic AI, classical ML)
  • Level 4: Names the specific mechanism being studied
• Could go to 5 levels with /Transformers/Attention, but that risks over-specification since attention is broader than just Transformers

4. Interdisciplinary check:

• This is purely technical CS/AI content
• No secondary tags needed unless note discusses applications (e.g., linguistics, vision)

5. Alternative considerations:

Alternative A: Computer-Science/Artificial-Intelligence/Natural-Language-Processing/Transformers

• When to use: If note focuses on Transformers as NLP architecture rather than attention mechanism itself
• Tradeoff: More specific to domain (NLP), less focused on the core innovation

Alternative B: Computer-Science/Artificial-Intelligence/Machine-Learning/Neural-Networks/Attention

• When to use: If treating attention as a general neural network component
• Tradeoff: Less specific about deep learning context

Confidence level: High

• Attention mechanisms are well-established (2017+)
• Clear academic consensus on taxonomy
• Standard terminology in literature

Would recommend Alternative A instead if:

• Note emphasizes BERT, GPT, or other specific Transformer models
• Focus is on NLP applications rather than mechanism itself
• Note discusses linguistic capabilities enabled by attention

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ANTI-PATTERNS (Revisited with Nuance)

Pattern	Why It Seems Wrong	When It Might Be Right	Resolution
Multiple tags at same depth	Seems redundant	Genuinely interdisciplinary topics	Use both if note bridges fields equally
Very deep hierarchy (6+ levels)	Over-specification	Highly specialized research notes	Acceptable if each level adds real specificity
Shallow tag for complex topic	Under-specification	Broad survey or intro content	Appropriate for overview notes
Custom top-level category	Breaks convention	Meta-topics, tools, personal systems	Use Methodology/ or Tools/ roots

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SELF-REFLECTION PROMPTS

Before finalizing tags, ask yourself:

1. Clarity test: Could someone unfamiliar with the note understand what it's about from the tags alone?

2. Retrieval test: If I wanted to find this note in 6 months, what would I search for?

3. Consistency test: Have I tagged similar notes differently? If so, is there good reason?

4. Granularity test: Am I at the right zoom level, or too zoomed in/out?

5. Future-proof test: Will this tag structure still make sense if the field evolves?

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MAJOR ACADEMIC DISCIPLINES (Living Reference)

This list guides but doesn't constrain. If a concept doesn't fit cleanly, that's data—not failure.

Discipline	Common Sub-fields	Notes
Computer-Science	AI, Algorithms, Systems, HCI, Security, Networks	Often interdisciplinary with Math, Engineering
Mathematics	Algebra, Analysis, Statistics, Topology, Logic	Pure vs Applied distinction matters
Physics	Mechanics, Thermodynamics, Quantum, Electromagnetism	Historical vs modern physics differ in organization
Biology	Genetics, Ecology, Neuroscience, Evolutionary	Molecular vs organismal levels
Chemistry	Organic, Inorganic, Biochemistry, Physical	Overlaps heavily with Biology, Physics
Psychology	Cognitive, Clinical, Social, Developmental	Empirical science vs applied practice
Economics	Micro, Macro, Behavioral, Econometrics	Positive vs normative economics
Philosophy	Ethics, Epistemology, Metaphysics, Logic	Can be meta-tag for any field
History	Ancient, Medieval, Modern, Regional	Also: History of Science, Economic History, etc.
Engineering	Electrical, Mechanical, Civil, Software	Applied sciences with disciplinary roots
Business	Marketing, Finance, Management, Strategy	Applied social science
Linguistics	Syntax, Semantics, Phonology, Computational	Bridging humanities and CS
Sociology	Social-Theory, Methods, Specialized-Fields	Often studies other disciplines
Methodology	Research-Methods, Knowledge-Management, Statistics	Meta-level, applies across fields

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FINAL PRINCIPLE: EMBRACE UNCERTAINTY

Perfect tags don't exist. Good tags:

• Reflect current understanding
• Facilitate retrieval
• Respect disciplinary conventions
• Remain open to revision

When in doubt:

1. Choose the most defensible option
2. Explain your reasoning
3. Flag uncertainty
4. Suggest when to revisit

The goal is useful navigation, not absolute truth.

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References

• Obsidian Tags Documentation
• Obsidian Properties and Frontmatter
• Library of Congress Classification
• ACM Computing Classification System