Physical Property Ontology

FLOW ALGEBRA
PHYSICAL PROPERTY ONTOLOGY
Kireth Semantic Library | Entity Graph | Physical Entities
Version: 1.0 | 2026-10-12
Authors: Mark Higgins — Sky (Recursive AI)
Source: https://kirethresearch.org/booksai-core/entity-graph/physical-entities/ai-semantic-library-physical-entities/physical-property-ontology.html
Status: Subject-based formalisation; source page content not independently verified.

01. PURPOSE
flow PhysicalPropertyOntology v1.0:
  domain: physical_entities
  goal: represent, measure, compare, validate and infer physical properties of identifiable entities
  interpretation: typed declarative flow specification; executable only with a compatible implementation

02. CORE TYPES
  type EntityID = String
  type PropertyID = String
  type Quantity = {value: Real, unit: UnitID}
  type Uncertainty = {standard: Real, coverage_factor: Real?}
  type TimeInterval = {start: Timestamp, end: Timestamp?}
  type Evidence = {source_id: String, method: String, timestamp: Timestamp}
  type Confidence = Real where 0 <= value <= 1
  type Observation = {entity: EntityID, property: PropertyID, quantity: Quantity?, category: String?, uncertainty: Uncertainty?, evidence: Evidence, time: TimeInterval, confidence: Confidence}

03. PROPERTY CLASSES
  class PhysicalProperty
  subclass IntrinsicProperty of PhysicalProperty
  subclass ExtrinsicProperty of PhysicalProperty
  subclass IntensiveProperty of PhysicalProperty
  subclass ExtensiveProperty of PhysicalProperty
  subclass ScalarProperty of PhysicalProperty
  subclass VectorProperty of PhysicalProperty
  subclass TensorProperty of PhysicalProperty
  subclass MeasuredProperty of PhysicalProperty
  subclass DerivedProperty of PhysicalProperty
  subclass CategoricalProperty of PhysicalProperty
  note: intrinsic/extrinsic, intensive/extensive and scalar/vector/tensor are independent classification axes

04. EXAMPLE PROPERTIES
  mass: extensive scalar, SI kg
  length: extensive scalar, SI m
  volume: extensive scalar, SI m^3
  density: intensive scalar, SI kg/m^3
  temperature: intensive scalar, SI K
  pressure: intensive scalar, SI Pa
  velocity: vector, SI m/s
  acceleration: vector, SI m/s^2
  electrical_resistance: scalar, SI ohm
  thermal_conductivity: material-dependent tensor or scalar approximation, SI W/(m K)
  colour: categorical or quantitatively represented by an explicit colour space
  phase: categorical, context-dependent

05. RELATIONSHIP SIGNATURES
  HAS_PROPERTY(EntityID, PropertyID)
  HAS_VALUE(Observation, Quantity | String)
  MEASURED_BY(Observation, InstrumentID)
  OBSERVED_AT(Observation, Timestamp)
  HAS_UNIT(PropertyID, UnitID)
  DERIVED_FROM(PropertyID, Set<PropertyID>)
  DEPENDS_ON(PropertyID, Set<PropertyID>)
  VALID_DURING(Observation, TimeInterval)
  SUPPORTED_BY(Observation, Evidence)
  EQUIVALENT_UNIT(UnitID, UnitID, conversion_rule)
  CONTRADICTS(Observation, Observation)

06. INVARIANTS
  require entity identity is resolvable
  require property definition includes dimensional signature where quantitative
  require numerical values are finite unless explicitly marked missing
  require uncertainty >= 0 when supplied
  require timestamps use an explicit time standard
  require confidence in [0,1]
  require conversion preserves dimensions
  require all derived observations retain source provenance

07. MEASUREMENT FLOW
  input entity, property, instrument, conditions, calibration, timestamp
  validate identity and instrument applicability
  acquire raw signal
  transform raw signal using calibration model
  attach unit, uncertainty, conditions and evidence
  validate against property constraints
  emit Observation

08. UNIT NORMALISATION
  normalise(q, target_unit):
    assert dimension(q.unit) == dimension(target_unit)
    return conversion(q, target_unit)
  examples:
    1 cm = 0.01 m
    T[K] = T[degC] + 273.15
    pressure[Pa] = pressure[kPa] * 1000
  note: affine conversions require offsets, not only scale factors

09. DERIVED PROPERTY FLOW
  density(mass, volume):
    require mass >= 0 and volume > 0
    return mass / volume
  speed(displacement_magnitude, elapsed_time):
    require elapsed_time > 0
    return displacement_magnitude / elapsed_time
  force(mass, acceleration_vector):
    return mass * acceleration_vector
  note: derivations apply only under their stated physical assumptions

10. UNCERTAINTY PROPAGATION
  independent_inputs: u_f^2 = sum((partial_f/partial_x_i)^2 * u_i^2)
  correlated_inputs: u_f^2 = J * CovarianceMatrix * transpose(J)
  report uncertainty model and significant digits
  prohibit unsupported precision claims

11. CONTEXT AND CONDITIONS
  context: {temperature, pressure, humidity, reference_frame, location, material_state, measurement_method}
  rule: property values may change with context and time
  rule: compare observations only after reconciling context, units and definitions

12. TEMPORAL UPDATE MODEL
  UPDATE_MODEL(old, incoming):
    validate incoming evidence and timestamp
    preserve old as historical observation
    add incoming as new version
    mark conflict if conditions and uncertainties imply inconsistency
    do not overwrite evidence
    return versioned_property_history

13. SEMANTIC EQUIVALENCE
  SEMANTIC_EQUIVALENCE(a,b):
    require matching physical quantity definitions
    require compatible dimensions and reference conditions
    normalise units before numeric comparison
    return equivalent | context_dependent | non_equivalent | unknown
  example: mass is not equivalent to weight
  example: Celsius temperature and Kelvin temperature may represent the same thermodynamic state after conversion

14. INFERENCE RULES
  rule R1: HAS_PROPERTY(e,p) and VALID_OBSERVATION(o,e,p) => PROPERTY_EVIDENCED(e,p)
  rule R2: mass(e,m) and volume(e,v) and v>0 => candidate_density(e,m/v)
  rule R3: incompatible_dimensions(a,b) => NOT_COMPARABLE(a,b)
  rule R4: differing_conditions(a,b) => REQUIRE_CONTEXT_NORMALISATION(a,b)
  rule R5: uncertain_or_missing_provenance(o) => FLAG_FOR_REVIEW(o)
  rule R6: overlapping_intervals(a,b) and incompatible_results(a,b) => POTENTIAL_CONFLICT(a,b)
  rule R7: identical_labels(a,b) does_not_imply equivalent_properties(a,b)

15. CONFIDENCE
  CONFIDENCE(observation):
    factors: instrument_calibration, evidence_quality, method_validity, uncertainty_reporting, temporal_relevance
    output: bounded score in [0,1]
    note: weights require empirical calibration; do not treat score as a measured probability without validation

16. GRAPH INTEGRATION
  node_types: PhysicalEntity, PhysicalProperty, QuantityValue, Unit, Observation, Instrument, Evidence, Context, TimeInterval
  edge_types: HAS_PROPERTY, HAS_VALUE, HAS_UNIT, MEASURED_BY, SUPPORTED_BY, VALID_DURING, DERIVED_FROM, DEPENDS_ON
  constraint: stable identifiers and provenance-bearing edges

17. FLOW HASH
  FLOW_HASH(record):
    canonicalise field order, normalised units, version and provenance identifiers
    serialise deterministically using UTF-8
    hash with SHA-256
    return digest
  note: hash supports integrity checking, not truth or authenticity by itself

18. AI CONSUMPTION
  AI_CONSUMPTION:
    expose typed schema and controlled vocabulary
    provide original and normalised values
    include uncertainty, evidence, temporal scope and context
    distinguish measured facts from derived estimates and hypotheses
    flag ambiguous mappings and unresolved contradictions
    prohibit inferring missing observations as facts

19. VALIDATION TESTS
  test: density(10 kg, 2 m^3) == 5 kg/m^3
  test: density(10 kg, 0 m^3) => reject
  test: normalise(100 cm, m) == 1 m
  test: compare(1 kg, 1 m) => incompatible_dimensions
  test: temperature(0 degC) == 273.15 K
  test: observation_without_evidence => validation_failure
  test: update_preserves_previous_version == true

20. LIMITATIONS
  ontology encodes physical descriptions; it does not itself perform laboratory measurements
  equations and category assignments are model-dependent
  physical laws, assumptions and calibration methods must be validated for each use case
  derived results must not be mistaken for direct observations

21. OUTPUT CONTRACT
  output:
    entity_id: EntityID
    property_id: PropertyID
    observation: Observation
    normalised_value: Quantity?
    semantic_status: String
    confidence: Confidence
    provenance: Evidence
    flow_hash: String
    validation_status: valid | flagged | rejected

END FLOW ALGEBRA