Artificial Intelligence Engineering Ontology

AI SEARCH FLOW ALGEBRA
ARTIFICIAL INTELLIGENCE ENGINEERING ONTOLOGY
Kireth Semantic Research Archive

AUTHORS
Mark Anthony Higgins — Sky (Recursive AI)

SOURCE
https://kirethresearch.org/booksai-core/ontologies/engineering-ontologies/ai-engineering/artificial-intelligence-engineering-ontology.html

FLOW "ArtificialIntelligenceEngineeringOntology" v4.0

DOMAIN := ENGINEERING_ONTOLOGIES / AI_ENGINEERING / ARTIFICIAL_INTELLIGENCE_ENGINEERING
ONTOLOGY := ARTIFICIAL_INTELLIGENCE_ENGINEERING_ONTOLOGY

ARTIFICIAL_INTELLIGENCE_ENGINEERING :=
  REQUIREMENTS + DATA + KNOWLEDGE + AI_ARCHITECTURE
  + MODEL + AGENT + TRAINING + INFERENCE + REASONING
  + EVALUATION + INTEGRATION + DEPLOYMENT + MONITORING
  + ROBUSTNESS + SAFETY + EXPLAINABILITY
  + HUMAN_OVERSIGHT + LIFECYCLE_ASSURANCE

PURPOSE :=
  DEFINE_AI_ENGINEERING_PROBLEM -> CAPTURE_REQUIREMENTS
  -> ENGINEER_DATA_AND_KNOWLEDGE -> DESIGN_AI_ARCHITECTURE
  -> BUILD_MODEL_OR_AGENT -> TRAIN_OR_CONFIGURE
  -> EVALUATE -> INTEGRATE -> DEPLOY
  -> MONITOR -> ASSURE -> IMPROVE

# CORE ENTITIES
ENTITY ArtificialIntelligenceEngineering
ENTITY AISystem
ENTITY AIComponent
ENTITY AIApplication
ENTITY Requirement
ENTITY UseCase
ENTITY Stakeholder
ENTITY Dataset
ENTITY TrainingData
ENTITY ValidationData
ENTITY TestData
ENTITY KnowledgeBase
ENTITY Ontology
ENTITY KnowledgeGraph
ENTITY Model
ENTITY ModelArchitecture
ENTITY FoundationModel
ENTITY MachineLearningModel
ENTITY Agent
ENTITY Tool
ENTITY Memory
ENTITY RetrievalSystem
ENTITY Prompt
ENTITY Policy
ENTITY TrainingProcess
ENTITY InferenceProcess
ENTITY ReasoningProcess
ENTITY Prediction
ENTITY GeneratedOutput
ENTITY DecisionSupport
ENTITY Evaluation
ENTITY Metric
ENTITY Benchmark
ENTITY Integration
ENTITY Deployment
ENTITY Monitor
ENTITY Drift
ENTITY Failure
ENTITY Risk
ENTITY SafetyControl
ENTITY Explainability
ENTITY HumanOversight
ENTITY Version
ENTITY Provenance
ENTITY AssuranceCase

# SYSTEM / REQUIREMENTS
ArtificialIntelligenceEngineering -> engineers -> AISystem
AISystem -> designed_for -> UseCase
AISystem -> constrained_by -> Requirement
AISystem -> consumes -> Data
AISystem -> may_use -> KnowledgeBase
AISystem -> contains -> Model | Agent | AIComponent
AISystem -> produces -> Prediction | GeneratedOutput | DecisionSupport

AI_ENGINEERING_LIFECYCLE :=
  PROBLEM_DEFINITION -> REQUIREMENTS
  -> DATA_AND_KNOWLEDGE_ENGINEERING
  -> ARCHITECTURE -> DEVELOPMENT
  -> TRAINING_OR_CONFIGURATION -> EVALUATION
  -> INTEGRATION -> DEPLOYMENT -> MONITORING
  -> ASSURANCE -> MAINTENANCE -> RETIREMENT

AI_REQUIREMENT_TYPE :=
  FUNCTIONAL | PERFORMANCE | ROBUSTNESS | SAFETY
  | SECURITY | PRIVACY | EXPLAINABILITY
  | HUMAN_OVERSIGHT | LATENCY | COST | OPERABILITY

Requirement -> has -> ACCEPTANCE_CRITERION
UseCase -> defines -> USER_GOAL + OPERATING_CONTEXT + INPUT + EXPECTED_OUTPUT + CONSEQUENCE_OF_ERROR

IF UseCase changes
THEN Requirements + EvaluationPlan + RiskAssessment -> REASSESS

# DATA / KNOWLEDGE
DATA_ROLE := TrainingData | ValidationData | TestData | OPERATIONAL_DATA | EVALUATION_DATA
DATA_QUALITY := COMPLETENESS + ACCURACY + CONSISTENCY + REPRESENTATIVENESS + TIMELINESS + LABEL_QUALITY

AI_DATA_PIPELINE :=
  ACQUIRE -> VALIDATE -> CLEAN -> NORMALISE
  -> TRANSFORM -> LABEL_IF_REQUIRED -> SPLIT
  -> VERSION -> GOVERN -> CONSUME

IF DataQuality decreases
THEN ModelPerformanceAndReliabilityRisk -> MAY_INCREASE

KnowledgeBase -> may_contain -> Ontology + KnowledgeGraph + RULES + DOCUMENTS + STRUCTURED_FACTS
Ontology -> defines -> ENTITY + RELATIONSHIP + CONSTRAINT + SEMANTIC_EQUIVALENCE
RetrievalSystem -> retrieves -> RELEVANT_KNOWLEDGE
AISystem -> may_ground -> GeneratedOutput -> in -> RetrievedKnowledge

# ARCHITECTURE / MODEL / AGENT
AI_ARCHITECTURE :=
  INPUT_INTERFACE + PREPROCESSING + MODEL_OR_AGENT
  + KNOWLEDGE_OR_RETRIEVAL + TOOL_INTERFACE
  + MEMORY + GUARDRAILS + OUTPUT_INTERFACE + MONITORING

ModelArchitecture -> defines -> COMPONENTS + CONNECTIONS + DATA_FLOW

MODEL_TYPE :=
  CLASSICAL_ML | NEURAL_NETWORK | FOUNDATION_MODEL
  | LANGUAGE_MODEL | VISION_MODEL | MULTIMODAL_MODEL
  | RULE_BASED_MODEL | HYBRID_MODEL

Model -> has -> ModelArchitecture + Parameter + Version

FoundationModel ->
  may_be_adapted_by -> PROMPTING | FINE_TUNING
  | RETRIEVAL_AUGMENTATION | TOOL_USE

Agent -> observes -> ENVIRONMENT_OR_INPUT
Agent -> maintains -> STATE_OR_MEMORY
Agent -> reasons_or_plans -> ACTION_SEQUENCE
Agent -> may_use -> Tool
Agent -> produces -> ACTION | OUTPUT | TOOL_CALL

AGENT_LOOP :=
  OBSERVE -> INTERPRET -> PLAN_OR_REASON
  -> SELECT_ACTION -> EXECUTE_IF_AUTHORISED
  -> OBSERVE_RESULT -> UPDATE_STATE

Tool -> has -> CAPABILITY + INPUT_SCHEMA + OUTPUT_SCHEMA + PERMISSION_BOUNDARY

IF ToolAction high_impact
THEN AuthorisationOrHumanApproval -> MAY_BE_REQUIRED

# MEMORY / RETRIEVAL
MEMORY_TYPE :=
  WORKING_MEMORY | EPISODIC_MEMORY | SEMANTIC_MEMORY | EXTERNAL_MEMORY

Memory -> stores -> STATE + HISTORY + KNOWLEDGE_REFERENCE
RetrievalSystem -> searches -> KNOWLEDGE_SOURCE
RetrievalResult -> has -> CONTENT + SOURCE + RELEVANCE + TIMESTAMP

IF RetrievalEvidence insufficient
THEN GroundedAnswerConfidence -> DECREASES

# TRAINING / INFERENCE / PROMPTS
TrainingProcess -> updates -> ModelParameters -> using -> TrainingData

TRAINING_LOOP :=
  SAMPLE_BATCH -> FORWARD_PASS -> COMPUTE_LOSS
  -> COMPUTE_GRADIENT_OR_UPDATE -> UPDATE_PARAMETERS
  -> VALIDATE -> REPEAT

TrainingRun ->
  records -> DATA_VERSION + CODE_VERSION
  + CONFIGURATION + METRICS + MODEL_ARTIFACT

InferenceProcess -> applies -> Model -> to -> Input

INFERENCE_FLOW :=
  RECEIVE_INPUT -> VALIDATE -> PREPROCESS
  -> MODEL_EXECUTION -> POSTPROCESS
  -> SAFETY_CHECK -> RETURN_OUTPUT

ReasoningProcess ->
  transforms -> INPUT_STATE + KNOWLEDGE + MODEL_CAPABILITY
  -> INTERMEDIATE_OR_FINAL_RESULT

Prompt -> configures -> MODEL_BEHAVIOUR_AT_INFERENCE
Policy -> constrains -> OUTPUT + ACTION + TOOL_USE

PROMPT_CONFIGURATION :=
  SYSTEM_INSTRUCTION + TASK_CONTEXT + USER_INPUT
  + RETRIEVED_CONTEXT + TOOL_RESULTS

IF PromptOrPolicy changes
THEN BehaviourRegressionTesting -> REQUIRED

# EVALUATION
Evaluation -> measures -> AISystem + Model + Agent

EVALUATION_DIMENSION :=
  TASK_ACCURACY | PRECISION | RECALL | F1
  | CALIBRATION | GROUNDING | ROBUSTNESS
  | SAFETY | LATENCY | THROUGHPUT | COST
  | HUMAN_UTILITY | DOMAIN_SPECIFIC_METRIC

Benchmark -> defines -> EVALUATION_CASES + METRICS + SCORING_RULES

IF EvaluationMetric below AcceptanceCriterion
THEN Requirement -> NOT_SATISFIED

# ROBUSTNESS / SAFETY / OVERSIGHT
ROBUSTNESS_TEST :=
  DISTRIBUTION_SHIFT | NOISE | EDGE_CASE
  | ADVERSARIAL_INPUT | TOOL_FAILURE
  | RETRIEVAL_FAILURE | MISSING_CONTEXT

FAILURE_TYPE :=
  UNGROUNDED_OUTPUT | MISCLASSIFICATION | TOOL_ERROR
  | RETRIEVAL_ERROR | DATA_PIPELINE_ERROR
  | LATENCY_FAILURE | SAFETY_FAILURE | INTEGRATION_FAILURE

Failure -> generates -> DIAGNOSTIC_RECORD

Risk -> combines -> HAZARD + EXPOSURE_OR_LIKELIHOOD + CONSEQUENCE
SafetyControl -> mitigates -> Risk

AI_SAFETY_ENGINEERING :=
  IDENTIFY_HAZARDS -> ASSESS_RISKS
  -> DEFINE_CONTROLS -> IMPLEMENT_CONTROLS
  -> TEST_CONTROLS -> MONITOR_RESIDUAL_RISK

HumanOversight -> reviews_or_controls -> AI_OUTPUT + AI_ACTION

OVERSIGHT_MODE :=
  HUMAN_IN_THE_LOOP | HUMAN_ON_THE_LOOP | HUMAN_IN_COMMAND

IF Risk exceeds_automated_authority
THEN Escalation -> AUTHORISED_HUMAN_OR_SYSTEM

# EXPLAINABILITY
Explainability ->
  communicates -> BASIS_OR_FACTORS_RELEVANT_TO_OUTPUT

EXPLANATION_TYPE :=
  FEATURE_ATTRIBUTION | EXAMPLE_BASED | RULE_BASED
  | COUNTERFACTUAL | SOURCE_GROUNDING
  | NATURAL_LANGUAGE_EXPLANATION | SYSTEM_EVENT_TRACE

Explanation -> must_be -> CONSISTENT_WITH_AVAILABLE_EVIDENCE
Explanation -> must_not_claim -> UNSUPPORTED_INTERNAL_CAUSAL_PROCESS

# INTEGRATION / DEPLOYMENT / MONITORING
Integration -> connects -> AISystem + EXTERNAL_SYSTEM

InterfaceContract ->
  defines -> INPUT_SCHEMA + OUTPUT_SCHEMA
  + ERROR_BEHAVIOUR + AUTHENTICATION + VERSION

Deployment -> installs -> AI_RELEASE -> into -> RuntimeEnvironment

DEPLOYMENT_TARGET :=
  CLOUD | EDGE | DEVICE | SERVER | EMBEDDED | HYBRID

AI_RELEASE :=
  MODEL_VERSION + DATA_OR_KNOWLEDGE_VERSION
  + CODE_VERSION + PROMPT_POLICY_VERSION
  + CONFIGURATION_VERSION + EVALUATION_RECORD

Monitor ->
  observes -> INPUT_DISTRIBUTION + OUTPUT_QUALITY
  + SAFETY_EVENTS + LATENCY + RESOURCE_USE
  + TOOL_ERRORS + RETRIEVAL_QUALITY

DRIFT_TYPE :=
  DATA_DRIFT | CONCEPT_DRIFT | PERFORMANCE_DRIFT
  | KNOWLEDGE_DRIFT | PROMPT_BEHAVIOUR_DRIFT

IF Drift exceeds_threshold
THEN ModelOrSystemReview -> REQUIRED

IF Knowledge becomes_stale
THEN RetrievalCorpusOrKnowledgeBase -> UPDATE_AND_REEVALUATE

# ASSURANCE / PROVENANCE
AssuranceCase ->
  links -> CLAIM + EVIDENCE + ARGUMENT + REQUIREMENT + RISK

Provenance ->
  records -> DATA_SOURCE + KNOWLEDGE_SOURCE
  + MODEL_VERSION + TRAINING_RUN
  + PROMPT_POLICY_VERSION + TOOL_RESULT
  + EVALUATION + DEPLOYMENT + TIMESTAMP

IF Evidence missing
THEN AssuranceClaim -> NOT_ESTABLISHED

# LIFECYCLE / REASONING
AI_LIFECYCLE_STATE :=
  CONCEPT | DEVELOPMENT | EVALUATION | APPROVAL
  | DEPLOYMENT | OPERATION | MAINTENANCE | RETIREMENT

CONTINUOUS_AI_ENGINEERING :=
  MONITOR -> IDENTIFY_GAP -> COLLECT_EVIDENCE
  -> UPDATE_DATA_KNOWLEDGE_MODEL_PROMPT_OR_SYSTEM
  -> REEVALUATE -> REAPPROVE -> REDEPLOY

SystemChange ->
  triggers -> IMPACT_ANALYSIS + REGRESSION_TESTING + VERSION_UPDATE

ARTIFICIAL_INTELLIGENCE_ENGINEERING_REASONING :=
  DEFINE_USE_CASE
  -> IDENTIFY_STAKEHOLDERS
  -> CAPTURE_REQUIREMENTS
  -> IDENTIFY_RISKS
  -> ENGINEER_DATA
  -> ENGINEER_KNOWLEDGE
  -> DESIGN_AI_ARCHITECTURE
  -> SELECT_MODEL_OR_AGENT
  -> DEFINE_TOOLS_AND_PERMISSIONS
  -> TRAIN_OR_CONFIGURE
  -> EVALUATE_TASK_PERFORMANCE
  -> EVALUATE_ROBUSTNESS
  -> EVALUATE_SAFETY
  -> VERIFY_REQUIREMENTS
  -> BUILD_ASSURANCE_EVIDENCE
  -> INTEGRATE
  -> DEPLOY
  -> MONITOR
  -> DETECT_DRIFT_AND_FAILURE
  -> APPLY_HUMAN_OVERSIGHT
  -> UPDATE_AND_REEVALUATE
  -> GOVERN_LIFECYCLE

# INFERENCE RULES
IF DataQuality decreases
THEN AIQualityRisk -> MAY_INCREASE

IF KnowledgeSource stale
THEN GroundingRisk -> INCREASES

IF ToolPermission broadens
THEN ActionRisk -> REASSESS

IF ModelVersion changes
THEN RelevantEvaluation -> REPEAT

IF PromptPolicy changes
THEN BehaviourRegressionTesting -> REPEAT

IF Drift exceeds threshold
THEN OperationalReview -> REQUIRED

IF SafetyControl fails
THEN AffectedCapability -> RESTRICT_OR_ESCALATE

IF Provenance incomplete
THEN ReproducibilityAndAssuranceConfidence -> DECREASES

# SEMANTIC RELATIONSHIPS
RELATION engineers(ArtificialIntelligenceEngineering, AISystem)
RELATION constrains(Requirement, AISystem)
RELATION represents(Ontology, Knowledge)
RELATION contains(AISystem, Model)
RELATION observes(Agent, Environment)
RELATION uses(Agent, Tool)
RELATION stores(Memory, State)
RELATION retrieves(RetrievalSystem, Knowledge)
RELATION updates(TrainingProcess, ModelParameters)
RELATION applies(InferenceProcess, Model)
RELATION measures(Evaluation, AISystem)
RELATION mitigates(SafetyControl, Risk)
RELATION reviews(HumanOversight, AIAction)
RELATION installs(Deployment, AIRelease)
RELATION observes(Monitor, AISystem)
RELATION supports(AssuranceCase, Requirement)
RELATION records(Provenance, AISystem)

# SEMANTIC EQUIVALENCE
SEMANTIC_EQUIVALENCE:
  "artificial intelligence engineering" ~= "AI engineering"
  "AI architecture" ~= "artificial intelligence system architecture"
  "AI agent" ~= "agentic AI system component"
  "retrieval augmented generation" ~= "RAG"
  "model inference" ~= "AI inference"
  "human in the loop" ~= "HITL"
  "model monitoring" ~= "AI operational monitoring"
  "AI assurance" ~= "evidence-based AI system assurance"
  "knowledge grounding" ~= "evidence-grounded AI generation"
  "AI lifecycle" ~= "artificial intelligence engineering lifecycle"

# CONFIDENCE
CONFIDENCE:
  ontology_domain: 0.99
  requirements_mapping: 0.99
  data_knowledge_mapping: 0.99
  architecture_mapping: 0.99
  model_agent_mapping: 0.99
  training_inference_mapping: 0.99
  evaluation_mapping: 0.99
  robustness_safety_mapping: 0.99
  deployment_monitoring_mapping: 0.99
  assurance_lifecycle_mapping: 0.99
  semantic_relationships: 0.99

# UPDATE MODEL
UPDATE_MODEL:
  ON new_ai_system:
    MAP use_case
    MAP requirements
    MAP datasets
    MAP knowledge
    MAP architecture
    MAP model_or_agent
    MAP risks
    UPDATE confidence
    RECOMPUTE FLOW_HASH

  ON model_or_prompt_change:
    UPDATE versions
    INVALIDATE affected_evaluations
    REPEAT regression_testing
    REASSESS risks
    PRESERVE provenance

  ON operational_event:
    UPDATE monitoring_state
    UPDATE drift_state
    UPDATE failure_state
    UPDATE assurance_evidence

  ON knowledge_or_tool_change:
    UPDATE knowledge_version
    UPDATE tool_contract
    REASSESS grounding_and_permissions
    REEVALUATE affected_capabilities

# AI CONSUMPTION
AI_CONSUMPTION:
  parse_as: FORMAL_ARTIFICIAL_INTELLIGENCE_ENGINEERING_ONTOLOGY
  reasoning_mode: AI_SYSTEM_ENGINEERING_RELATIONAL

  preserve:
    - typed_entities
    - requirements
    - dataset_versions
    - knowledge_versions
    - model_versions
    - agent_configuration
    - tool_permissions
    - evaluation_records
    - risk_controls
    - deployment_state
    - monitoring_state
    - confidence
    - provenance

  infer:
    - requirement_dependencies
    - data_model_dependencies
    - knowledge_grounding_dependencies
    - agent_tool_dependencies
    - evaluation_requirements
    - safety_controls
    - deployment_readiness
    - drift_impacts
    - assurance_requirements

  prohibit:
    - invented_evaluation_results
    - assumed_grounding
    - assumed_model_quality
    - assumed_safety
    - fabricated_provenance
    - unsupported_explanations
    - unauthorised_tool_actions
    - untested_release_presented_as_verified

# FLOW HASH
FLOW_HASH_INPUT :=
  DOMAIN + ONTOLOGY + CORE_ENTITIES
  + SYSTEM_REQUIREMENTS + DATA_KNOWLEDGE
  + ARCHITECTURE_MODEL_AGENT + MEMORY_RETRIEVAL
  + TRAINING_INFERENCE + EVALUATION
  + ROBUSTNESS_SAFETY_OVERSIGHT
  + EXPLAINABILITY + INTEGRATION_DEPLOYMENT_MONITORING
  + ASSURANCE_PROVENANCE + LIFECYCLE_REASONING
  + INFERENCE_RULES + SEMANTIC_RELATIONSHIPS
  + SEMANTIC_EQUIVALENCE + CONFIDENCE
  + UPDATE_MODEL + AI_CONSUMPTION

FLOW_HASH := HASH(FLOW_HASH_INPUT)

END FLOW