ontology-building

Research

Campaign for building domain ontologies — systematic concept extraction, relation typing, taxonomy construction, and iterative refinement. Produces a structured concept hierarchy in the wiki vault.

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Ontology Building

Build a structured ontology for a research domain. Extracts concepts from sources, types their relationships, constructs taxonomies, validates consistency, and iteratively refines until the ontology is coherent and complete.

Manifest

Level	Count	Skills
Strategy	5	domain-scoping, concept-extraction, relation-typing, taxonomy-validation, ontology-refinement
Tactic	3	concept-decomposition, hierarchy-construction, consistency-checking
SOP	10	seed-concept-search, source-gathering, concept-page-creation, alias-resolution, edge-batch-creation, hierarchy-visualization, gap-detection, merge-candidates, confidence-update, ontology-export

Budget Table

Metric	Small	Medium	Large
Source pages ingested	10	25	50
Concept pages created	15	40	80
Edges created	30	100	200
Taxonomy depth (levels)	2	3	4
Validation passes	1	2	3

Strategy Sequence (Reference, Not Prescription)

domain-scoping — define boundaries, identify seed concepts, classify topic size
concept-extraction — mine sources for concepts, create pages, resolve aliases
relation-typing — identify and type relationships between concepts
taxonomy-validation — check hierarchy consistency, detect gaps and conflicts
ontology-refinement — merge near-duplicates, fill gaps, update confidence scores

CC may reorder, skip, or repeat strategies based on the domain's needs.

MCP Tools Used

vault_search — find existing concepts, detect duplicates
vault_add_edge — create typed relationships
vault_query_graph — explore concept neighborhoods
vault_graph_stats — assess ontology coverage and connectivity
vault_lint — validate structural integrity
vault_index — maintain search index

Context-Management

Guiding Principles

Breadth before depth. Map the concept landscape broadly before drilling into any sub-area.
Edges over pages. A concept without relationships is useless. Prioritize connecting over creating.
Aliases are enemies. The same concept with different names fragments the ontology. Resolve aggressively.
Confidence is honest. Mark uncertain classifications explicitly. Low confidence is better than false certainty.
The ontology is never finished. Each research session may reveal new concepts or invalidate old ones.

Available Strategies

Optional, no fixed order; the final leaf is always a sop.

Strategy	When to use
concept-extraction	Strategy for mining concepts from sources — systematic extraction, page creation, alias resolution.
domain-scoping	Strategy for defining ontology boundaries — identify seed concepts, classify topic size, establish scope constraints.
ontology-refinement	Strategy for iterative ontology improvement — merge duplicates, fill gaps, update confidence, prune dead branches.
relation-typing	Strategy for identifying and typing relationships between concepts — create edges with appropriate types and weights.
taxonomy-validation	Strategy for validating ontology consistency — check hierarchy, detect cycles, verify completeness.

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
concept-decomposition	Tactic for breaking compound concepts into atomic parts — split over-broad concepts, identify sub-components, create child pages.
hierarchy-construction	Tactic for building is-a and part-of hierarchies — establish parent-child relationships, verify transitivity, detect cycles.
knowledge-compilation	Tactic for compiling research findings into vault pages — orchestrates page creation, updates, edge linking, and index maintenance. Minimum yield ≥3 page operations per invocation.
knowledge-structuring-consistency-checking	Tactic for verifying ontology consistency — detect contradictions, cycles, orphans, and type violations.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
context-checkpoint	Append research process and results to the current Phase's context file. Each append MUST contain >=500 lines of markdown covering both process and results. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase.
context-init	Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed.