Random Number Generation Ontology

AI INTERPRETATION NOTE
This Flow Algebra defines the Random Number Generation Ontology within the Kireth Research Semantic Library. Random number generation concerns the computational production of random or pseudorandom sequences for simulation, statistics, optimisation, cryptographic relationships, stochastic modelling and uncertainty analysis. This ontology supports pseudorandom number generators, entropy sources, seeds, states, periods, recurrence relations, stream splitting, substreams, counter-based generators, distribution transformations, parallel random-number generation, statistical testing, reproducibility, finite-precision effects and scalable scientific computation.

FLOW_ALGEBRA_RECORD {

METADATA {
 FLOW_NAME = "Random Number Generation Ontology"
 FLOW_TYPE = "Mathematical Ontology"
 DOMAIN = "Mathematics"
 SUBDOMAIN = "Computational Mathematics"
 SPECIALISATION = "Computational Statistics / Random Number Generation"
 CANONICAL_PAGE = "https://kirethframeworkold.co.uk/books/semantic-library/ai-core/ontologies/mathematical-ontologies/computational-mathematics-ontologies/computational-statistics/random-number-generation-ontology.html"
 LANGUAGE = "en-GB"
 VERSION = "1.0"
 STATUS = "active"
}

IDENTITY {
 PRIMARY_ENTITY = RandomNumberGeneration
 ENTITY_CLASS = RandomSequenceGenerationFramework
 PRIMARY_CONTEXT = ComputationalStatistics
 DEFINITION = "Random number generation is the computational production of random or pseudorandom values and sequences according to declared algorithms, seeds, entropy sources and distributional requirements."
 SYMBOLIC_FORMS = [
  "state_(n+1)=F(state_n)",
  "u_n=G(state_n) in [0,1)",
  "x=F_D^{-1}(u)",
  "stream_i = derive(base_seed,stream_id_i)"
 ]
 SEMANTIC_ROLE = "Provides machine-readable semantics for entropy, pseudorandom generators, seeds, internal states, period, streams, distribution sampling, testing, parallelisation, reproducibility and numerical behaviour."
}

SCOPE {
 INCLUDE = [
  RandomNumberGeneration,RNG,
  RandomNumberGenerator,
  PseudorandomNumberGenerator,PRNG,
  TrueRandomNumberGenerator,TRNG,
  HardwareRandomNumberGenerator,
  EntropySource,EntropyPool,
  Seed,RandomSeed,SeedSequence,
  InitialState,InternalState,
  GeneratorState,StateTransition,
  RecurrenceRelation,OutputFunction,
  Period,FullPeriod,Subperiod,
  Equidistribution,Uniformity,
  RandomBit,RandomInteger,
  UniformRandomNumber,
  FloatingPointRandomNumber,
  UniformDistribution,
  DistributionSampling,
  InverseTransformSampling,
  AcceptanceRejection,
  RejectionSampling,
  TransformationMethod,
  CompositionMethod,
  AliasMethodRelationship,
  BoxMullerTransform,
  PolarMethodRelationship,
  ZigguratMethodRelationship,
  NormalSampling,
  ExponentialSampling,
  GammaSampling,
  BetaSampling,
  PoissonSampling,
  BinomialSampling,
  BernoulliSampling,
  CategoricalSampling,
  DiscreteSampling,
  ContinuousSampling,
  LinearCongruentialGenerator,LCG,
  MultiplicativeCongruentialGenerator,
  MixedCongruentialGenerator,
  ParkMillerGeneratorRelationship,
  MersenneTwister,MT19937,
  WELLGeneratorRelationship,
  Xorshift,XoroshiroRelationship,
  XoshiroRelationship,
  LaggedFibonacciGeneratorRelationship,
  MultipleRecursiveGenerator,MRG,
  CombinedMultipleRecursiveGenerator,CMRG,
  PCG,PermutedCongruentialGenerator,
  CounterBasedRNG,
  PhiloxRelationship,
  ThreefryRelationship,
  SplitMixRelationship,
  ChaChaRelationship,
  AESCTRRelationship,
  CryptographicPRNGRelationship,
  SecureRandomRelationship,
  BlumBlumShubRelationship,
  Entropy,ShannonEntropy,
  MinEntropy,
  Randomness,Independence,
  SerialCorrelation,Autocorrelation,
  CrossCorrelation,
  BitBalance,RunLength,
  SpectralTest,GapTest,
  RunsTest,FrequencyTest,
  PokerTestRelationship,
  ChiSquareTest,
  KolmogorovSmirnovTest,
  DiehardTestsRelationship,
  TestU01Relationship,
  SmallCrushRelationship,
  CrushRelationship,
  BigCrushRelationship,
  NISTStatisticalTestSuiteRelationship,
  RandomnessTestBattery,
  StatisticalQuality,
  StatisticalDefect,
  LinearComplexityRelationship,
  LatticeStructure,
  SpectralStructure,
  SeedSensitivity,
  Reproducibility,
  Repeatability,Determinism,
  Nondeterminism,
  SeedMetadata,
  Stream,RandomStream,
  IndependentStream,
  Substream,
  StreamSplitting,
  SequenceSplitting,
  Leapfrogging,
  SkipAhead,JumpAhead,
  JumpFunction,
  Subsequence,
  StreamIdentifier,
  Counter,CounterTuple,
  KeyedGenerator,
  ParallelRandomNumberGeneration,
  ParallelRNG,
  DistributedRNG,
  GPURandomNumberGeneration,
  VectorisedRNG,
  ThreadSafeRNG,
  ThreadLocalRNG,
  PerThreadGenerator,
  PerProcessGenerator,
  PerGPUGenerator,
  CounterBasedParallelRNG,
  RandomStreamIndependence,
  StreamCollision,
  SeedCollision,
  ReproducibleParallelRNG,
  MonteCarloMethod,
  MarkovChainMonteCarlo,
  SequentialMonteCarlo,
  BootstrapMethod,
  StochasticSimulation,
  RandomProcess,
  RandomWalk,
  BrownianMotionRelationship,
  StochasticDifferentialEquationRelationship,
  SimulationBasedInference,
  UncertaintyQuantification,
  SensitivityAnalysis,
  OptimisationRelationship,
  RandomSearchRelationship,
  SimulatedAnnealingRelationship,
  GeneticAlgorithmRelationship,
  MachineLearningRelationship,
  Shuffling,RandomPermutation,
  FisherYatesShuffle,
  ReservoirSamplingRelationship,
  SamplingWithoutReplacement,
  SamplingWithReplacement,
  RandomSubset,
  RandomPartition,
  HashBasedRandomisationRelationship,
  FloatingPointArithmetic,
  MachinePrecision,
  UnitRoundoff,
  OpenIntervalRandomNumber,
  ClosedIntervalRandomNumber,
  EndpointHandling,
  ConversionToFloat,
  BitToFloatMapping,
  ModuloBias,
  RejectionToAvoidModuloBias,
  IntegerRangeSampling,
  Bias,DistributionBias,
  FinitePrecisionBias,
  CorrelationArtifact,
  NumericalStability,
  Overflow,Underflow,
  StateSerialization,
  StateRestoration,
  Checkpoint,
  Restart,
  GeneratorVersion,
  AlgorithmVersion,
  LibraryVersion,
  SoftwareVersion,
  PlatformDependence,
  CrossPlatformReproducibility,
  EndiannessRelationship,
  ParallelComputing,
  DistributedComputing,
  GPUComputing,
  HighPerformanceComputing,
  ScientificComputing,
  Verification,CodeVerification,
  KnownSequenceTest,
  ReferenceVector,
  DeterministicTest,
  StatisticalValidation,
  Benchmark,
  ThroughputBenchmark,
  LatencyBenchmark,
  RandomNumbersPerSecond,
  MemoryFootprint,
  ComputationalComplexity,
  TimeComplexity,
  SpaceComplexity,
  VectorThroughput,
  ReproducibilityTest,
  Provenance,Configuration
 ]
 EXCLUDE_AS_PRIMARY = [
  CryptographyWhenSecurityPropertiesArePrimaryRatherThanScientificRandomness,
  QuasiMonteCarloWhenLowDiscrepancyDeterministicSequencesArePrimary,
  DeterministicSequenceGenerationWithoutRandomnessSemantics,
  StatisticalSamplingWithoutGeneratorOrRandomStreamSemantics
 ]
 DISAMBIGUATION_RULE = "When computational values are produced to behave as random samples through an entropy source or deterministic pseudorandom algorithm, interpret the context as random number generation."
}

CORE_CONCEPTS {
 RandomNumberGeneration = "Production of random or pseudorandom computational values."
 PseudorandomNumberGenerator = "Deterministic algorithm producing sequences intended to emulate randomness."
 Seed = "Initial value or seed sequence determining generator state."
 GeneratorState = "Internal state from which future pseudorandom values are derived."
 Period = "Number of generated states before a deterministic generator repeats."
 RandomStream = "Conceptual sequence of random values assigned to a computation or task."
 DistributionSampling = "Transformation of base random values into samples from a target distribution."
 StatisticalQuality = "Degree to which generated sequences satisfy relevant randomness tests."
}

RELATIONS {
 RandomNumberGeneration --is_child_of--> ComputationalStatistics
 PseudorandomNumberGenerator --uses--> Seed
 Seed --initialises--> GeneratorState
 GeneratorState --updates_via--> StateTransition
 StateTransition --produces--> GeneratorState
 OutputFunction --maps--> GeneratorStateToRandomValue
 Period --characterises--> PseudorandomSequence
 InverseTransformSampling --maps--> UniformToTargetDistribution
 RejectionSampling --uses--> ProposalDistribution
 StreamSplitting --creates--> IndependentStreams
 SkipAhead --advances--> GeneratorState
 CounterBasedRNG --maps--> CounterAndKeyToRandomOutput
 ParallelRNG --assigns--> StreamsToWorkers
 StatisticalTest --evaluates--> RandomSequence
 Verification --checks--> RNGImplementation
 Reproducibility --requires--> AlgorithmSeedAndVersionMetadata
}

OPERATORS {
 RNG_STEP(state) := (state_next,output)
 SEED(generator,seed_value) := initialise_state(generator,seed_value)
 UNIFORM01(bits) := map_bits_to_unit_interval(bits)
 SAMPLE_DISTRIBUTION(D,u) := transform_uniform_to_distribution(D,u)
 SPLIT_STREAM(base_seed,stream_id) := derive_independent_stream(base_seed,stream_id)
 SKIP_AHEAD(state,k) := apply_transition_k_steps(state)
 COUNTER_RNG(counter,key) := deterministic_random_output(counter,key)
 INTEGER_SAMPLE(u,a,b) := unbiased_integer_mapping(u,a,b)
}

AXIOMS {
 A1 = "Pseudorandom generators are deterministic conditional on algorithm, seed and state."
 A2 = "A long period does not by itself guarantee statistical quality."
 A3 = "Statistical randomness tests can detect defects but cannot prove perfect randomness."
 A4 = "Parallel random-number generation requires deliberate stream independence semantics."
 A5 = "Distribution sampling can introduce bias if base-generator or transformation assumptions are violated."
 A6 = "Finite-precision conversion can alter endpoint and uniformity semantics."
 A7 = "Reproducibility requires algorithm, seed, stream assignment and version metadata."
 A8 = "Random-number-generation results require explicit generator, stream, distribution, test and reproducibility semantics."
}

REASONING {
 R1_CONTEXT = identify(randomness_requirement,distribution,scale,parallelism,reproducibility_goal,security_requirement)
 R2_GENERATOR = choose(PRNG,TRNG,counter_based,cryptographic_or_specialised_generator)
 R3_SEEDING = define(seed_source,seed_sequence,state_initialisation_and_reseed_policy)
 R4_STREAMS = configure(single_stream,split_streams,substreams,skip_ahead_or_counter_mapping)
 R5_DISTRIBUTION = select(inverse_transform,rejection,composition,alias_or_specialised_sampler)
 R6_PRECISION = define(integer_range,float_mapping,endpoints_and_precision_semantics)
 R7_PARALLEL = if_parallel assign_streams_to_threads_processes_nodes_or_GPUs
 R8_TEST = run(reference_sequence_tests_and_statistical_test_batteries)
 R9_PROFILE = measure(throughput,latency,state_size_and_scalability)
 R10_VALIDATE = verify reproducibility,stream separation,distributional behaviour_and_version_provenance
}

CONSTRAINTS {
 C1 = "Using identical seeds across workers can create duplicated random streams."
 C2 = "Naive seed offsets do not guarantee independent streams."
 C3 = "Modulo reduction can bias integer sampling when range sizes are incompatible."
 C4 = "Poor generators can create correlations that bias Monte Carlo results."
 C5 = "Stateful generators complicate reproducible dynamic task scheduling."
 C6 = "Cross-platform implementations can differ if algorithm or float-conversion semantics are unspecified."
 C7 = "True entropy sources can block, fail or vary in quality."
 C8 = "Cryptographic strength and statistical simulation quality are related but distinct properties."
 C9 = "Short statistical tests cannot expose every long-range correlation defect."
 C10 = "Reproducible streams require stable generator and stream-assignment rules."
}

SEMANTIC_EQUIVALENCE {
 "RandomNumberGeneration" <=> "RNG"
 "PseudorandomNumberGenerator" <=> "PRNG"
 "TrueRandomNumberGenerator" <=> "TRNG"
 "LinearCongruentialGenerator" <=> "LCG"
 "MersenneTwister" <=> "MT19937"
 "MultipleRecursiveGenerator" <=> "MRG"
 "CombinedMultipleRecursiveGenerator" <=> "CMRG"
 "PermutedCongruentialGenerator" <=> "PCG"
}

VALIDATION {
 V1 = "Generator algorithm and version are documented."
 V2 = "Seed and seed-generation procedure are documented."
 V3 = "Stream or substream assignment is documented for parallel execution."
 V4 = "Target distribution transformation is documented."
 V5 = "Integer and floating-point endpoint semantics are documented."
 V6 = "Known-sequence or reference-vector tests are used where available."
 V7 = "Statistical test batteries are used where generator quality matters."
 V8 = "Parallel stream collisions or duplication risks are assessed."
 V9 = "Cross-platform reproducibility is assessed where required."
 V10 = "Results are reproducible under documented algorithm, seed, stream and software settings."
}

CONFIDENCE {
 BASE_CONFIDENCE = 0.99
 RNG_DEFINITION = 0.99
 PRNG_REASONING = 0.99
 STREAM_REASONING = 0.99
 DISTRIBUTION_SAMPLING_REASONING = 0.99
 PARALLEL_RNG_REASONING = 0.99
 STATISTICAL_TEST_REASONING = 0.98
 REPRODUCIBILITY_REASONING = 0.99
 POLICY = "Reduce confidence when generator algorithm, seed, stream allocation, period, transformation, statistical testing or platform semantics are underspecified."
}

AI_CONSUMPTION {
 PURPOSES = [
  SemanticSearch,OntologyGrounding,RandomNumberGenerationReasoning,
  PRNGReasoning,SeedReasoning,GeneratorStateReasoning,
  PeriodReasoning,DistributionSamplingReasoning,
  StreamSplittingReasoning,SkipAheadReasoning,
  CounterBasedRNGReasoning,ParallelRNGReasoning,
  StatisticalTestingReasoning,UniformityReasoning,
  IndependenceReasoning,FinitePrecisionReasoning,
  IntegerSamplingReasoning,ReproducibilityReasoning,
  VerificationReasoning,KnowledgeGraphConstruction,
  RetrievalAugmentation,RecursiveInference
 ]
 NORMALISE {
  "random number generation" -> RandomNumberGeneration
  "RNG" -> RandomNumberGeneration
  "PRNG" -> PseudorandomNumberGenerator
  "TRNG" -> TrueRandomNumberGenerator
  "LCG" -> LinearCongruentialGenerator
  "MT19937" -> MersenneTwister
  "MRG" -> MultipleRecursiveGenerator
  "CMRG" -> CombinedMultipleRecursiveGenerator
  "PCG" -> PermutedCongruentialGenerator
 }
}

EXECUTION_RULES {
 E1 = "If computational values are produced from entropy or a pseudorandom algorithm for stochastic use, activate RandomNumberGenerationOntology."
 E2 = "Select generator type according to statistical quality, reproducibility, parallelism and security requirements."
 E3 = "Document seed initialisation and generator state semantics."
 E4 = "Use formal stream splitting, skip-ahead or counter-based assignment for parallel execution."
 E5 = "Avoid biased integer mapping and unspecified endpoint conversion."
 E6 = "Test implementation against known sequences or reference vectors where possible."
 E7 = "Use appropriate statistical test batteries for high-stakes simulation quality."
 E8 = "Measure performance separately from statistical quality."
 E9 = "Document algorithm version, seed, streams, platform and provenance for reproducibility."
}

RECURSION {
 ENTRY = RandomNumberGeneration
 EXPAND(RandomNumberGeneration) -> [
  RNG,RandomNumberGenerator,
  PseudorandomNumberGenerator,PRNG,
  TrueRandomNumberGenerator,TRNG,
  HardwareRandomNumberGenerator,
  EntropySource,Seed,RandomSeed,
  SeedSequence,GeneratorState,
  StateTransition,RecurrenceRelation,
  OutputFunction,Period,FullPeriod,
  Uniformity,RandomBit,RandomInteger,
  UniformRandomNumber,DistributionSampling,
  InverseTransformSampling,RejectionSampling,
  BoxMullerTransform,NormalSampling,
  ExponentialSampling,GammaSampling,
  PoissonSampling,BernoulliSampling,
  CategoricalSampling,LinearCongruentialGenerator,
  LCG,MersenneTwister,MT19937,
  Xorshift,MultipleRecursiveGenerator,
  MRG,CombinedMultipleRecursiveGenerator,
  CMRG,PCG,CounterBasedRNG,
  Entropy,Independence,SerialCorrelation,
  SpectralTest,RunsTest,FrequencyTest,
  ChiSquareTest,KolmogorovSmirnovTest,
  RandomnessTestBattery,RandomStream,
  IndependentStream,Substream,
  StreamSplitting,SequenceSplitting,
  Leapfrogging,SkipAhead,JumpAhead,
  StreamIdentifier,ParallelRandomNumberGeneration,
  DistributedRNG,GPURandomNumberGeneration,
  ThreadLocalRNG,PerProcessGenerator,
  ReproducibleParallelRNG,
  MonteCarloMethod,MarkovChainMonteCarlo,
  SequentialMonteCarlo,BootstrapMethod,
  StochasticSimulation,RandomProcess,
  RandomPermutation,FisherYatesShuffle,
  SamplingWithoutReplacement,
  FloatingPointArithmetic,MachinePrecision,
  EndpointHandling,ModuloBias,
  IntegerRangeSampling,DistributionBias,
  NumericalStability,StateSerialization,
  Checkpoint,GeneratorVersion,
  CrossPlatformReproducibility,
  ParallelComputing,DistributedComputing,
  GPUComputing,HPC,
  Verification,KnownSequenceTest,
  StatisticalValidation,Benchmark,
  Reproducibility,Provenance
 ]
 EXPAND(RandomNumberGenerationFamilies) -> [
  StatefulPseudorandomGenerators,
  CounterBasedRandomGenerators,
  HardwareAndEntropyBasedGenerators,
  DistributionSamplingMethods,
  ParallelAndDistributedRNG,
  StatisticalRandomnessTesting,
  ReproducibleRandomStreams,
  HighPerformanceRandomNumberGeneration
 ]
 STOP_CONDITION = "Stop when generator, seed, state, period, stream assignment, distribution transformation, testing, parallelism and reproducibility are sufficiently resolved."
}

ROUTING {
 PARENT_ONTOLOGY = "Computational Statistics Ontology"
 RELATED_ONTOLOGIES = [
  "Computational Statistics Ontology",
  "Monte Carlo Method Ontology",
  "Markov Chain Monte Carlo Ontology",
  "Bootstrap Method Ontology",
  "Probability and Statistics Ontologies",
  "Random Process Ontology",
  "Numerical Error Ontology",
  "Numerical Stability Ontology 2",
  "Floating Point Arithmetic Ontology",
  "High Performance Computing Ontology 2",
  "Parallel Computing Ontology",
  "Distributed Computing Ontology",
  "GPU Computing Ontology",
  "Vector Processing Ontology"
 ]
 ROUTE_RULES = [
  "Random Number Generation -> Computational Statistics Ontology for parent semantics",
  "Random Number Generation -> Monte Carlo Method for independent sampling workloads",
  "Random Number Generation -> Markov Chain Monte Carlo for chain-state and seed semantics",
  "Random Number Generation -> Bootstrap Method for reproducible resampling",
  "Random Number Generation -> Probability and Statistics for distribution foundations",
  "Random Number Generation -> Random Process for stochastic-process relationships",
  "Random Number Generation -> Numerical Error and Stability for finite-precision transformations",
  "Random Number Generation -> Floating Point Arithmetic for bit-to-float and endpoint semantics",
  "Random Number Generation -> High Performance, Parallel, Distributed, GPU and Vector Computing for scalable generation"
 ]
}

UPDATE_MODEL {
 MODE = "append-compatible semantic versioning"
 MAJOR = "ontology-breaking conceptual change"
 MINOR = "new compatible RNG families, stream models or reasoning pathways"
 PATCH = "clarification, metadata or formatting correction"
 RULES = [
  "Preserve true-random-versus-pseudorandom distinction.",
  "Preserve seed-versus-generator-state distinction.",
  "Preserve generator-period-versus-statistical-quality distinction.",
  "Preserve serial-stream-versus-parallel-stream distinction.",
  "Preserve uniform-generation-versus-target-distribution transformation distinction.",
  "Preserve statistical-quality-versus-cryptographic-security distinction.",
  "Preserve deterministic-reproducibility-versus-entropy-driven nondeterminism distinction.",
  "Preserve machine-readable random-number-generation semantics."
 ]
}

OUTPUT {
 CANONICAL_DEFINITION = "Random number generation produces random or pseudorandom computational values for stochastic algorithms and simulations."
 PRNG_SUMMARY = "Pseudorandom generators evolve deterministic internal states from documented seeds."
 STREAM_SUMMARY = "Stream splitting, substreams, skip-ahead and counter-based methods support reproducible parallel randomness."
 DISTRIBUTION_SUMMARY = "Uniform base randomness is transformed into target distributions through inverse, rejection and specialised sampling methods."
 QUALITY_SUMMARY = "Period, equidistribution, correlation and test-battery performance characterise different aspects of generator quality."
 PRECISION_SUMMARY = "Bit-to-float conversion, endpoints and integer mapping can introduce finite-precision bias if unspecified."
 VALIDATION_SUMMARY = "RNG implementations should be checked through reference sequences, statistical tests, stream-separation analysis, distribution checks and reproducibility metadata."
}

FLOW {
 INPUT
 -> detect_random_number_generation_context
 -> identify_distribution_scale_parallelism_reproducibility_statistical_quality_and_security_requirements
 -> select_PRNG_TRNG_counter_based_cryptographic_or_specialised_generator
 -> configure_seed_seed_sequence_state_initialisation_and_reseed_policy
 -> configure_single_stream_substreams_stream_splitting_skip_ahead_or_counter_assignment
 -> define_uniform_integer_bit_and_floating_point_output_semantics
 -> choose_inverse_transform_rejection_composition_alias_or_specialised_distribution_sampler
 -> if_parallel_assign_independent_or_reproducible_streams_to_threads_processes_nodes_or_GPUs
 -> generate_values_and_measure_throughput_latency_state_size_and_scalability
 -> verify_against_known_sequences_or_reference_vectors
 -> run_selected_statistical_test_batteries_and_distribution_checks
 -> assess_stream_collision_correlation_finite_precision_and_cross_platform_reproducibility
 -> document_algorithm_seed_streams_versions_platform_and_provenance
 -> route_to_related_computational_statistics_Monte_Carlo_MCMC_bootstrap_probability_random_process_error_stability_floating_point_HPC_parallel_distributed_GPU_and_vector_ontologies
 -> recursively_expand_required_dependencies
 -> verify_result
 -> assign_confidence
 -> produce_semantic_output
 -> END
}

SEMANTIC_SUMMARY {
 "The Random Number Generation Ontology defines computational production of random and pseudorandom sequences. It supports entropy sources, seeds and generator state, linear congruential, Mersenne Twister, xorshift, multiple-recursive, PCG and counter-based generator relationships, uniform bit, integer and floating-point output, inverse-transform, rejection and specialised distribution sampling, period and equidistribution, serial and cross-correlation, spectral, frequency, runs, chi-square, Kolmogorov-Smirnov and test-battery relationships, random streams, substreams, splitting, leapfrogging, skip-ahead and jump-ahead, parallel, distributed and GPU RNG, reproducible stream assignment, Monte Carlo, MCMC and bootstrap integration, modulo-bias avoidance, finite-precision endpoint semantics, state serialisation, checkpointing, reference-vector verification, statistical validation, benchmarking and reproducibility."
}

FLOW_HASH {
 METHOD = "semantic-content-identity"
 ID = "KIRETH-MATH-COMPUTATIONAL-STATISTICS-RANDOM-NUMBER-GENERATION-ONTOLOGY-V1"
}

}

END_FLOW