Knowledge Transfer Domain Ontology

FLOW ALGEBRA
TITLE: Knowledge Transfer Domain Ontology
VERSION: 1.0
DOMAIN: Education / Knowledge Transfer
SOURCE_URL: https://kirethresearch.org/booksai-core/ontologies/domain-ontologies/education-domains/knowledge-transfer-domain-ontology.html
STATUS: Domain-based draft; source page content not independently verified
AUTHORS: Mark Higgins — Sky (Recursive AI)

1. PURPOSE
Represent the movement, interpretation, validation, retention and application of knowledge between people, teams, institutions and computational systems. Distinguish transfer of information from demonstrated understanding and effective application.

2. TYPED ENTITIES
KnowledgeAsset: {id, title, knowledge_type, subject, version, provenance, licence, language}
KnowledgeSource: {id, actor_type, expertise_domain, authority_scope}
KnowledgeRecipient: {id, actor_type, prior_knowledge, accessibility_needs}
TransferEvent: {id, source_id, recipient_id, asset_id, channel_id, time, context_id}
TransferChannel: {id, mode, synchronous:boolean, medium, accessibility_features}
LearningContext: {id, domain, environment, prerequisites, constraints}
KnowledgeRepresentation: {id, format, vocabulary, ontology_ref, fidelity}
Competency: {id, skill, level, evidence_requirement}
Assessment: {id, method, criterion, observation, assessor, timestamp}
Feedback: {id, event_id, observation, recommendation}
Barrier: {id, barrier_type, severity, mitigation}
Outcome: {id, recipient_id, competency_id, observed_application, evidence}

3. RELATIONSHIP TYPES
PROVIDES(source:KnowledgeSource, asset:KnowledgeAsset)
RECEIVES(recipient:KnowledgeRecipient, asset:KnowledgeAsset)
TRANSFERRED_VIA(event:TransferEvent, channel:TransferChannel)
OCCURS_IN(event:TransferEvent, context:LearningContext)
REPRESENTED_AS(asset:KnowledgeAsset, representation:KnowledgeRepresentation)
REQUIRES(asset:KnowledgeAsset, competency:Competency)
ASSESSED_BY(outcome:Outcome, assessment:Assessment)
GENERATES_FEEDBACK(event:TransferEvent, feedback:Feedback)
OBSTRUCTED_BY(event:TransferEvent, barrier:Barrier)
DERIVED_FROM(asset:KnowledgeAsset, source:KnowledgeAsset)
APPLIES(recipient:KnowledgeRecipient, asset:KnowledgeAsset)

4. FLOW MODEL
INPUT = validated knowledge asset + identified source + recipient + context
PRECONDITIONS = permissions + prerequisites + accessible representation
TRANSFER = encode -> communicate -> receive -> interpret -> practise
VALIDATION = assess comprehension -> test application -> collect feedback
OUTPUT = evidenced competency or documented transfer gap
FEEDBACK_LOOP = revise representation -> adjust channel -> reassess

5. INFERENCE RULES
RULE KT-01: IF transfer_event recorded AND delivery confirmed THEN transfer_status = delivered; DO NOT infer comprehension.
RULE KT-02: IF assessment meets stated criterion AND evidence is valid THEN comprehension_status = supported.
RULE KT-03: IF recipient demonstrates competency in target context THEN application_status = demonstrated.
RULE KT-04: IF prerequisite missing THEN recommend prerequisite learning before advanced transfer.
RULE KT-05: IF barrier detected THEN recommend an accessible or alternative channel; preserve original asset provenance.
RULE KT-06: IF asset version superseded THEN flag dependent transfer materials for review.
RULE KT-07: IF context differs materially from source context THEN flag transferability as uncertain until evaluated.
RULE KT-08: IF assessment evidence is absent THEN outcome_status = unverified.

6. CONFIDENCE
CONFIDENCE(statement) = calibrated estimate based on source reliability, evidence quality, assessment validity, recency and contextual fit.
Never interpret confidence as a guarantee. Store confidence values with evidence, method and timestamp. Missing evidence yields UNKNOWN, not zero certainty.

7. SEMANTIC_EQUIVALENCE
SEMANTIC_EQUIVALENCE(a,b) requires matched concept definition, scope, context and intended use.
Examples: 'knowledge sharing' overlaps with 'knowledge transfer' but does not always establish recipient understanding; 'training completion' is not equivalent to 'competency achieved'.

8. PROVENANCE
For each assertion record: source_id, asset_version, author_or_custodian, timestamp, transformation_history, assessment_reference and applicable licence.

9. FLOW_HASH
FLOW_HASH = SHA256(canonical_serialisation(ontology_id, version, entity_schema, relationship_schema, rules, provenance_policy)).
Canonicalisation requires deterministic field ordering and UTF-8 encoding. Recompute on semantic revision.

10. UPDATE_MODEL
TRIGGERS: changed knowledge asset, new evidence, revised competency standard, accessibility issue, feedback, or superseded ontology.
PROCESS: detect -> validate -> version -> map dependencies -> recompute FLOW_HASH -> publish change log -> notify consumers.
Retain historical versions and avoid silently overwriting assessment evidence.

11. AI_CONSUMPTION
Provide machine-readable typed entities, explicit predicates, source links, version identifiers, evidence grades and uncertainty markers.
Require AI consumers to distinguish transmitted content, inferred understanding and observed application. Do not invent learning outcomes or credentials.

12. QUALITY CONSTRAINTS
- Respect privacy, consent, access permissions and intellectual property.
- Avoid unsupported claims about learning efficacy.
- Preserve accessibility and language requirements.
- Separate descriptive evidence from normative recommendations.
- Validate transfer outcomes using context-appropriate evidence.

END FLOW ALGEBRA