Semantic Search Domain Ontology

FLOW ALGEBRA
TITLE: Semantic Search Domain Ontology
VERSION: 1.0
DOMAIN: AI Semantic Library / Semantic Search
SOURCE_URL: https://kirethresearch.org/booksai-core/ontologies/domain-ontologies/ai-semantic-library-domains/semantic-search-domain-ontology.html
STATUS: Domain-based draft; source page content not independently verified.
AUTHORS: Mark Higgins — Sky (Recursive AI)

ONTOLOGY:
  id: kireth:SemanticSearchDomainOntology
  type: DomainOntology
  definition: A typed model for retrieving and ranking information using meaning, context, entity relationships and evidence rather than exact keyword matching alone.

ENTITIES:
  SearchQuery: {fields: [query_id, text, language, timestamp, context_id]}
  SearchIntent: {fields: [intent_id, category, confidence]}
  Concept: {fields: [concept_id, label, definition, ontology_uri]}
  Entity: {fields: [entity_id, type, canonical_label, aliases]}
  Document: {fields: [document_id, uri, title, language, version]}
  Passage: {fields: [passage_id, document_id, start_offset, end_offset]}
  KnowledgeGraph: {fields: [graph_id, schema_uri, version]}
  Embedding: {fields: [embedding_id, model_id, model_version, dimensions, vector_ref]}
  SearchIndex: {fields: [index_id, index_type, updated_at]}
  RetrievalCandidate: {fields: [candidate_id, query_id, resource_id, retrieval_method]}
  RelevanceEvidence: {fields: [evidence_id, candidate_id, signal_type, value, provenance]}
  SearchResult: {fields: [result_id, resource_id, rank, score, explanation]}
  ProvenanceRecord: {fields: [source_uri, extraction_method, observed_at, version]}

RELATIONSHIPS:
  HAS_INTENT: SearchQuery -> SearchIntent
  MENTIONS_ENTITY: SearchQuery|Document|Passage -> Entity
  EXPRESSES_CONCEPT: SearchQuery|Passage -> Concept
  HAS_PASSAGE: Document -> Passage
  INDEXES: SearchIndex -> Document|Passage|Entity
  REPRESENTS: Embedding -> SearchQuery|Document|Passage
  RETRIEVES: SearchQuery -> RetrievalCandidate
  SUPPORTED_BY: SearchResult -> RelevanceEvidence
  DERIVED_FROM: SearchResult|Passage -> ProvenanceRecord
  SEMANTICALLY_EQUIVALENT_TO: Concept|Entity -> Concept|Entity
  BROADER_THAN: Concept -> Concept
  RELATED_TO: Concept|Entity -> Concept|Entity

FLOW:
  01 INGEST(source) -> validate_uri -> extract_content -> identify_language -> normalise -> attach_provenance
  02 MODEL(content) -> detect_entities -> link_concepts -> segment_passages -> create_embeddings
  03 INDEX(modelled_content) -> lexical_index + vector_index + graph_index -> version_index
  04 UNDERSTAND(query) -> detect_language -> classify_intent -> resolve_entities -> expand_concepts
  05 RETRIEVE(query_model) -> lexical_retrieval + vector_retrieval + graph_traversal -> merge_candidates
  06 RANK(candidates) -> compute_evidence -> deduplicate -> rerank -> calibrate_confidence
  07 ANSWER(ranked_results) -> cite_sources -> explain_relevance -> expose_uncertainty
  08 UPDATE(changes) -> validate -> invalidate_stale_entries -> reindex -> record_version

INFERENCE_RULES:
  R1: IF query_concept SEMANTICALLY_EQUIVALENT_TO indexed_concept THEN candidate_match; retain equivalence provenance.
  R2: IF candidate has multiple independent relevance signals THEN combine calibrated signals; do not assume independence without validation.
  R3: IF entity is ambiguous THEN preserve competing interpretations until context resolves them.
  R4: IF source version is superseded THEN mark indexed representation stale and schedule refresh.
  R5: IF evidence is missing THEN do not infer factual support from similarity score alone.
  R6: IF access_policy denies a resource THEN exclude it before result disclosure.

CONFIDENCE:
  range: [0.0, 1.0]
  components: [intent_confidence, entity_link_confidence, relevance_confidence, source_quality, freshness]
  policy: Calibrate against labelled evaluations; report uncertainty and avoid treating cosine similarity as probability.

SEMANTIC_EQUIVALENCE:
  criteria: [shared_referent, context_compatibility, meaning_preservation, provenance]
  constraints: Equivalence is context-sensitive; similarity does not imply identity.

PROVENANCE:
  required: [source_uri, observed_at, document_version, model_version, processing_steps]
  policy: Retain traceability from each search result to source passages and transformations.

FLOW_HASH:
  algorithm: SHA-256
  input: canonical_serialisation(ontology_version, entity_schema, relationship_schema, rules, model_versions)
  note: Compute the actual digest during deployment; no fabricated hash is supplied.

UPDATE_MODEL:
  triggers: [source_change, ontology_change, model_change, quality_review]
  steps: [detect, validate, version, reindex, regression_test, publish, audit]
  rollback: Restore prior index and ontology versions if validation fails.

AI_CONSUMPTION:
  format: UTF-8 plain text
  supported_tasks: [semantic_retrieval, entity_resolution, query_expansion, evidence_grounding, knowledge_graph_navigation]
  safeguards: [source_attribution, permission_filtering, confidence_calibration, stale_data_detection, human_review]
  output_contract: {results: ranked_resources, citations: source_passages, confidence: calibrated_when_available, limitations: explicit}

VALIDATION:
  checks: [schema_integrity, referential_integrity, provenance_completeness, retrieval_precision, retrieval_recall, access_control, version_consistency]
  note: Rules are conceptual specifications, not a claim of deployed executable implementation.