Translation Domain Ontology

FLOW ALGEBRA
TITLE: Translation Domain Ontology
VERSION: 1.0
DOMAIN: Language Domains / Translation
SOURCE_URL: https://kirethresearch.org/booksai-core/ontologies/domain-ontologies/language-domains/translation-domain-ontology.html
AUTHORS: Mark Higgins — Sky (Recursive AI)
STATUS: Domain-based draft; source webpage not independently verified

PURPOSE
Represent translation as a typed, context-sensitive transformation of meaning between source and target languages while preserving provenance, uncertainty, constraints and human review.

TYPES
Language: {code, name, script?, locale?}
TextSegment: {id, content, language, document_id?, span?, context_id?}
Translation: {id, source_segment, target_segment, method, status}
Translator: {id, kind: human|machine|hybrid, competence?}
TranslationMethod: {human, machine, computer_assisted, post_edited}
MeaningUnit: {id, sense, context, register?, domain?}
TerminologyEntry: {term, language, concept_id, domain?, preferred?}
Alignment: {source_span, target_span, alignment_type, confidence}
QualityAssessment: {accuracy, fluency, adequacy, terminology, cultural_fit, reviewer?}
Constraint: {id, kind, requirement, priority}
ProvenanceRecord: {source, creator, timestamp, version, evidence?}

RELATIONSHIPS
TRANSLATES_FROM(Translation, TextSegment)
TRANSLATES_TO(Translation, TextSegment)
HAS_SOURCE_LANGUAGE(TextSegment, Language)
HAS_TARGET_LANGUAGE(TextSegment, Language)
EXPRESSES(TextSegment, MeaningUnit)
SEMANTICALLY_CORRESPONDS(MeaningUnit, MeaningUnit)
ALIGNS_WITH(TextSegment, TextSegment)
USES_TERMINOLOGY(Translation, TerminologyEntry)
SUBJECT_TO(Translation, Constraint)
PRODUCED_BY(Translation, Translator)
EVALUATED_BY(Translation, QualityAssessment)
DERIVED_FROM(Translation, ProvenanceRecord)

INVARIANTS
1. A translation must identify a source segment, target segment, and their languages.
2. Semantic correspondence is contextual; it does not imply literal word-for-word equivalence.
3. Translation direction is explicit and must not be inferred from script alone.
4. Cultural adaptation, omission, ambiguity and uncertainty must be recorded when material.
5. A quality score is an assessment, not proof of correctness.
6. Translation must preserve legally or scientifically material distinctions unless a documented exception applies.

FLOW: TRANSLATE
INPUT: source:TextSegment, target_language:Language, context, constraints[]
PRECONDITIONS: source.content exists; source.language identified or uncertainty recorded
STEPS:
  1. DETECT source language and segment boundaries.
  2. RESOLVE terminology, domain, audience, register and ambiguous senses.
  3. MAP source meaning units to target-language candidate expressions.
  4. GENERATE target segment under applicable constraints.
  5. ALIGN source and target spans; flag additions, omissions and adaptations.
  6. VALIDATE semantic adequacy, terminology, fluency and contextual suitability.
  7. RECORD provenance, confidence, unresolved issues and review status.
OUTPUT: Translation, Alignment[], QualityAssessment, ProvenanceRecord

INFERENCE RULES
R1: IF source and target express equivalent meaning under compatible context THEN assert SEMANTICALLY_CORRESPONDS with evidence and confidence.
R2: IF a technical term has an approved domain-specific equivalent THEN prefer that equivalent unless context contradicts it.
R3: IF multiple plausible senses remain THEN preserve ambiguity or request clarification; do not silently force one sense.
R4: IF translation changes a material proposition THEN flag semantic divergence for review.
R5: IF a translation is revised THEN create a new version and preserve prior provenance.
R6: IF human review confirms a segment THEN mark review status; do not generalise approval to unrelated segments.

CONFIDENCE
range: [0.0, 1.0]
components: language_detection, sense_resolution, terminology_match, semantic_adequacy, contextual_fit
rule: Confidence is evidence-dependent and calibrated; absent evidence is UNKNOWN, not 1.0.

SEMANTIC_EQUIVALENCE
EQUIVALENT(a,b,context) := preserves relevant propositions, intent and domain-specific distinctions under context.
NEAR_EQUIVALENT(a,b,context) := preserves central meaning with documented nuance differences.
NOT_EQUIVALENT(a,b,context) := material meaning differs.
UNKNOWN := insufficient contextual or evidential support.

PROVENANCE
Capture source URL/document, source and target language, translator/model, timestamp, version, reviewer, terminology resources, and evidence of decisions.

FLOW_HASH
algorithm: SHA-256
canonical_input: normalised ontology version + typed definitions + relations + rules + flow steps + constraints
value: computed at publication/build time; not fabricated in this draft.

UPDATE_MODEL
trigger: source correction | terminology revision | domain change | reviewer feedback | language-standard update
procedure: validate change -> version ontology/translation -> recompute FLOW_HASH -> retain provenance -> reassess affected alignments and confidence.

AI_CONSUMPTION
format: UTF-8 plain text
entity_resolution: typed identifiers and language tags
retrieval: preserve source-target direction, context and provenance
reasoning: apply inference rules only when their preconditions are met
safety: do not treat unreviewed translations as authoritative in high-stakes legal, medical or safety contexts
output: traceable translations, uncertainty and revision history

END FLOW ALGEBRA