Physical Object Ontology

FLOW ALGEBRA
PHYSICAL OBJECT ONTOLOGY
Kireth Semantic Library | Entity Graph > Physical Entities > Fundamental Physical Entities
Version: 1.1 | Date: 2026-10-12 | Locale: en-GB
Source URL: https://kirethresearch.org/booksai-core/entity-graph/physical-entities/fundamental-physical-entities/physical-object-ontology.html
Authors: Mark Higgins — Sky (Recursive AI)
Status: Scientific synthesis; source page content not independently verified.

01. PURPOSE
Represent physical objects as persistent, spatially situated entities with measurable properties, constituent parts, interactions and histories. Distinguish physical objects from processes, regions, properties and abstract entities.

02. FORMAL SIGNATURE
sort Object, PhysicalEntity, Material, Part, Region, Time, Event, Property, Quantity, Unit, Frame, Observation, Agent.
Object <: PhysicalEntity.
predicate existsAt(Object, Time): Bool
predicate occupies(Object, Region, Time): Bool
predicate partOf(PhysicalEntity, Object, Time): Bool
predicate composedOf(Object, Material, Time): Bool
predicate participatesIn(Object, Event, Time): Bool
predicate hasProperty(Object, Property, Time): Bool
function measure(Object, Property, Time, Frame) -> Quantity?
function identity(Object) -> Identifier

03. CORE AXIOMS
A1: Object(o) => PhysicalEntity(o).
A2: occupies(o,r,t) => existsAt(o,t).
A3: partOf(p,o,t) => existsAt(o,t) AND existsAt(p,t), where p is a physical entity.
A4: partOf(p,o,t) AND partOf(o,q,t) => partOf(p,q,t), for proper or general parthood under a consistent mereological interpretation.
A5: identity(o) is stable across observations of the same object, even if its properties change.
A6: measured quantities require explicit units, reference frames where relevant, uncertainty and observation time.
A7: two observations with matching properties do not alone establish identical object identity.
A8: no object can be a proper part of itself at the same time.

04. ENTITY CLASSES
NaturalObject: rock, planet, crystal, organism.
ManufacturedObject: instrument, vehicle, component, structure.
CompositeObject: assembly of constituent objects.
SimpleModelObject: idealised point mass or rigid body; approximation, not literal ontology.
MicroscopicObject: molecule or atom under an explicitly chosen physical modelling scheme.
MacroscopicObject: everyday material body.

05. RELATIONSHIP TYPES
isA(Object, PhysicalEntity)
instanceOf(object, class)
properPartOf(part, whole, time)
connectedTo(objectA, objectB, time)
inside(object, regionOrContainer, time)
adjacentTo(objectA, objectB, time)
interactsWith(objectA, objectB, interaction, time)
causes(eventA, eventB) [requires evidence]
observedBy(object, instrument, observation)
derivedFrom(object, sourceObject, transformationEvent)

06. SPATIAL SEMANTICS
position(o,t,F) -> vector with length units
orientation(o,t,F) -> rotation representation
extent(o,t,F) -> bounded spatial region when applicable
velocity(o,t,F) = d(position)/dt when differentiable
Coordinate-dependent values must name frame F.
A location measurement is not a universal intrinsic property.

07. TEMPORAL SEMANTICS
lifetime(o) = {t | existsAt(o,t)}
state(o,t) = collection of valid time-indexed properties
transition(o,t1,t2) = change in state with identity continuity assessed
Creation, fragmentation, fusion and destruction require explicit identity policies.

08. MATERIAL COMPOSITION
composedOf(o,m,t) identifies material constituents.
mass(o,t) may vary through exchange of matter or energy.
volume(o,t,F) measures spatial extent under a stated model.
density(o,t) = mass(o,t) / volume(o,t) when volume > 0.
Do not assume every physical object has a sharp boundary.

09. PHYSICAL PROPERTIES
Intrinsic candidates: rest mass, charge, material composition (model dependent).
Extrinsic properties: position, velocity, temperature relative to measurement conditions, environment-dependent state.
Properties must be scoped to conditions and measurement method.

10. INTERACTIONS AND EVENTS
forceOn(o,t,F) -> vector (newtons)
netForce(o,t,F) = sum applied forces
For a constant-mass Newtonian model: netForce = mass * acceleration.
This relation is not a universal description of all physical systems.
Energy and momentum transfers are event-linked and system-boundary dependent.

11. MEREOLOGY
properPartOf(p,o,t) => partOf(p,o,t) AND p != o.
For a classical extensional mereology, partOf is reflexive, antisymmetric and transitive.
Distinct temporal slices must not be conflated with enduring whole objects.

12. IDENTITY AND PERSISTENCE
Identity evidence may include trajectory continuity, material continuity, serial identifiers and causal history.
Identity is not established solely by equal mass, shape or coordinates.
An object can survive limited component replacement under a declared persistence policy.

13. MEASUREMENT MODEL
Observation = (object_id, property, value, unit, uncertainty, time, frame, method, provenance).
Validation requires compatible dimensions and declared units.
Independent observations can disagree within measurement uncertainty.
Confidence refers to evidence quality, not metaphysical certainty.

14. MATHEMATICAL INVARIANTS
For an isolated system, total energy and momentum obey conservation laws under applicable physical assumptions.
For a closed classical system, total mass can be conserved to an appropriate approximation.
Conservation statements must specify system boundaries and theory.

15. INFERENCE RULES
R1: Object(o) & occupies(o,r,t) -> existsAt(o,t).
R2: properPartOf(p,o,t) -> partOf(p,o,t).
R3: partOf(p,o,t) & partOf(o,q,t) -> partOf(p,q,t).
R4: measure(o,mass,t,F)=q & unit(q)=kg -> dimension(q)=MASS.
R5: hasProperty(o,p,t1) & hasProperty(o,p2,t2) does not imply o changed identity.
R6: interactsWith(a,b,i,t) -> participatesIn(a,i,t) & participatesIn(b,i,t), if i is modelled as an event.

16. EXAMPLE: PHYSICAL INSTRUMENT
object: instrument_01
class: ManufacturedObject
material: aluminium alloy
mass: 2.40 kg +/- 0.02 kg
observation_time: 2026-10-12
part: sensor_01
relationship: properPartOf(sensor_01, instrument_01, t)
Validation: mass positive; uncertainty non-negative; instrument and sensor identifiers distinct.

17. EXAMPLE: MOVING OBJECT
Object o at t0 has x=0 m, velocity=3 m/s in laboratory frame F.
Assuming constant velocity for 4 s, x(t0+4 s)=12 m.
Inference is conditional on inertial-frame and constant-velocity assumptions.

18. COUNTEREXAMPLES
A colour property is not a physical object.
An empty coordinate region is not automatically a material object.
A process such as combustion is an event/process, not the fuel object.
A digital identifier names an object but is not the physical object itself.

19. SEMANTIC EQUIVALENCE
physical object ~ material body only in contexts restricting objects to material bodies.
physical object != physical entity (physical entity is broader).
partOf != locatedIn.
sameLocation != sameIdentity.
semantic equivalence requires matching type, context and temporal scope.

20. KNOWLEDGE GRAPH MAPPING
Node types: PhysicalObject, Material, Region, Event, Observation, Unit, Frame.
Edges: INSTANCE_OF, PART_OF, COMPOSED_OF, OCCUPIES, PARTICIPATES_IN, OBSERVED_BY.
Every time-varying edge carries valid_time; every evidence-based assertion carries provenance.

21. VALIDATION RULES
V1: Object identifiers are unique within namespace.
V2: Proper-part cycles are rejected at a single time.
V3: Quantity units conform to dimensional type.
V4: Time-indexed claims have valid timestamps or intervals.
V5: Uncertain observations include uncertainty representation.
V6: Frame-dependent vectors specify reference frame.
V7: Inferences record assumptions and source assertions.
V8: Contradictory assertions are flagged, not silently overwritten.

22. FAILURE MODES
Object/property confusion; inconsistent part-whole semantics; identity merging across observations; missing temporal scope; mixing classical and quantum descriptions; units omitted; inference without model assumptions.

23. CONFIDENCE
CONFIDENCE(assertion) in [0,1] denotes evidence confidence.
Do not equate confidence with physical probability without an explicit probabilistic model.
Evidence provenance, calibration, repeatability and model suitability inform confidence.

24. FLOW_HASH
FLOW_HASH := SHA256(canonicalised ontology payload excluding FLOW_HASH field).
Canonicalisation: UTF-8, LF newlines, stable field order, normalised identifiers.
Hash must be computed on the deployed payload; no fabricated digest is supplied.

25. UPDATE_MODEL
version: 1.1
change_type: additive-or-corrective
requires: provenance retention, semantic versioning, regression validation
update_policy: preserve historical assertions; publish superseding claims with timestamps.

26. AI_CONSUMPTION
machine_readable: true
preferred_tasks: entity extraction, knowledge graph alignment, physical reasoning, measurement validation, scientific retrieval
reasoning_guardrails: distinguish measured facts, assumptions, idealisations and inferred conclusions
output_requirements: source, units, temporal scope, reference frame, confidence where available.

27. DEPENDENCIES
Physical Entity Ontology; Matter Ontology; Mass Ontology; Energy Ontology; Space Ontology; Time Ontology; Measurement Ontology; Relationship Ontology; Entity Graph.

28. COMPLETENESS CHECK
[PASS] Entity typing and relationships
[PASS] Spatial and temporal semantics
[PASS] Mereology and identity
[PASS] Measurement and units
[PASS] Inference and examples
[PASS] Validation and counterexamples
[PASS] Semantic equivalence
[PASS] Confidence and provenance
[PASS] Flow Hash specification
[PASS] Update Model and AI Consumption

END FLOW ALGEBRA