yogsoth-ai/knowledge-structuring - Page 2

feedback-loop-detection

Tactic for identifying circular causation — detect feedback loops, classify as reinforcing or balancing, document loop structure.

Langue du texte source : anglais

model-gap-detection

SOP for finding gaps in the causal model — missing variables, unexplained effects, weak links.

Langue du texte source : anglais

validation-report

SOP for generating a causal model validation report — summarize coverage, confidence, gaps, contradictions.

Langue du texte source : anglais

evidence-collection

Gather evidence for causal claims

Langue du texte source : anglais

intervention-analysis

Analyze interventions and manipulations on the causal system

Langue du texte source : anglais

mechanism-mapping

Map causal mechanisms between variables

Langue du texte source : anglais

model-validation

Validate causal model consistency

Langue du texte source : anglais

variable-identification

Identify key variables in the causal system

Langue du texte source : anglais

causal-modeling

Campaign for building causal models — identify variables, map mechanisms, collect evidence, analyze interventions, validate models. Produces causal graphs in the wiki vault.

Langue du texte source : anglais

confidence-update

SOP for updating confidence scores on claims and evidence pages based on new information.

Langue du texte source : anglais

gap-detection

SOP for finding structural gaps in the ontology — missing concepts, thin branches, disconnected clusters.

Langue du texte source : anglais

hierarchy-visualization

SOP for inspecting the current hierarchy structure — query graph to display parent-child relationships.

Langue du texte source : anglais

merge-candidates

SOP for identifying near-duplicate concepts that should be merged.

Langue du texte source : anglais

ontology-export

SOP for exporting ontology summary — generate a readable overview of the current ontology state.

Langue du texte source : anglais

seed-concept-search

SOP for finding seed concepts in existing vault and web sources to anchor ontology construction.

Langue du texte source : anglais

source-gathering

SOP for gathering source material relevant to the ontology domain — fetch papers and web pages into source pages.

Langue du texte source : anglais

concept-decomposition

Tactic for breaking compound concepts into atomic parts — split over-broad concepts, identify sub-components, create child pages.

Langue du texte source : anglais

consistency-checking

Tactic for verifying ontology consistency — detect contradictions, cycles, orphans, and type violations.

Langue du texte source : anglais

hierarchy-construction

Tactic for building is-a and part-of hierarchies — establish parent-child relationships, verify transitivity, detect cycles.

Langue du texte source : anglais

concept-extraction

Strategy for mining concepts from sources — systematic extraction, page creation, alias resolution.

Langue du texte source : anglais

domain-scoping

Strategy for defining ontology boundaries — identify seed concepts, classify topic size, establish scope constraints.

Langue du texte source : anglais

ontology-refinement

Strategy for iterative ontology improvement — merge duplicates, fill gaps, update confidence, prune dead branches.

Langue du texte source : anglais

relation-typing

Strategy for identifying and typing relationships between concepts — create edges with appropriate types and weights.

Langue du texte source : anglais

taxonomy-validation

Strategy for validating ontology consistency — check hierarchy, detect cycles, verify completeness.

Langue du texte source : anglais

ontology-building

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

Langue du texte source : anglais