FRAM Ontology & JSON-LD

Each FRAM function is represented with its aspects, variability characteristics, and quantitative metadata.

{
  "@id": "fram:function_heat-water",
  "@type": ["Function", "TechnologicalFunction"],
  "name": "Heat Water",
  "description": "Function to heat water to desired temperature",
  "functionType": "Technological",
  
  "aspects": {
    "input": {
      "@type": "InputAspect",
      "semanticRole": "What activates the function",
      "connections": [...]
    },
    "output": {
      "@type": "OutputAspect",
      "semanticRole": "The result of the function"
    }
  },
  
  "variability": {
    "@type": "Variability",
    "potential": "Moderate",
    "hasPhenotype": [
      {"@type": "TimingPhenotype", "value": "OnTime", "probability": 0.70},
      {"@type": "TimingPhenotype", "value": "TooLate", "probability": 0.15},
      {"@type": "TimingPhenotype", "value": "TooEarly", "probability": 0.10},
      {"@type": "TimingPhenotype", "value": "NotAtAll", "probability": 0.05}
    ]
  },
  
  "quantitativeMetadata": {
    "constants": [...],
    "variables": [...],
    "outputs": [...],
    "interpretationProfile": {
      "input": "ALL",
      "precondition": "ALL",
      "resource": "ANY"
    }
  }
}

Couplings represent the connections between functions, capturing how outputs affect downstream aspects and how variability propagates.

{
  "@id": "fram:coupling_f1-f2",
  "@type": "Coupling",
  "label": "Heated water output",
  
  "sourceFunction": "fram:function_heat-water",
  "sourceFunctionName": "Heat Water",
  "targetFunction": "fram:function_serve-tea",
  "targetFunctionName": "Serve Tea",
  
  "sourceAspect": "OutputAspect",
  "targetAspect": "InputAspect",
  
  "couplingType": "Linear",
  "couplingStrength": 1.0,

  "variabilityPropagation": {
    "propagates": true,
    "amplification": 1.0,
    "description": "Variability from Heat Water may propagate to Serve Tea"
  }
}

For computable FRAM models, the ontology supports quantitative metadata including constants, variables, and activation conditions.

{
  "quantitativeMetadata": {
    "constants": [
      {
        "@type": "Constant",
        "name": "TEMP_THRESHOLD",
        "value": 95,
        "dataType": "xsd:decimal",
        "unit": "°C",
        "semanticMeaning": "Temperature threshold"
      }
    ],
    
    "variables": [
      {
        "@type": "Variable",
        "name": "CURRENT_TEMP",
        "expression": "INITIAL_TEMP + HEATING_RATE * TIME",
        "codeType": "expression",
        "sourceType": "calculated",
        "dependencies": ["INITIAL_TEMP", "HEATING_RATE", "TIME"]
      },
      {
        "@type": "Variable",
        "name": "ITERATION",
        "expression": "flow.get('sim_iteration') || 0",
        "sourceType": "external",
        "externalKey": "sim_iteration",
        "hasFallback": true,
        "fallbackValue": "0"
      }
    ],
    
    "activationCondition": "CURRENT_TEMP >= TEMP_THRESHOLD",
    "isCustomActivation": false,
    
    "interpretationProfile": {
      "@type": "InterpretationProfile",
      "input": "ALL",
      "precondition": "ALL",
      "resource": "ANY",
      "time": "NONE",
      "control": "NONE"
    }
  }
}

Performance variability is a first-class ontological entity. Phenotypes are modeled as independent OWL classes (siblings of Variability, not subclasses), with dedicated properties enabling quantitative resonance analysis via entropy calculations and probabilistic propagation. Quantification is method-agnostic and lives in node metadata, not in the ontology TBox.

owl:Thing
├── fram:FRAMModel
├── fram:Function
│   ├── fram:HumanFunction
│   ├── fram:TechnologicalFunction
│   ├── fram:OrganisationalFunction
│   ├── fram:BackgroundFunction
│   ├── fram:EntryFunction
│   ├── fram:ExitFunction
│   └── fram:ForegroundFunction
├── fram:Aspect
│   ├── fram:InputAspect
│   ├── fram:OutputAspect
│   ├── fram:PreconditionAspect
│   ├── fram:ResourceAspect
│   ├── fram:ControlAspect
│   └── fram:TimeAspect
├── fram:Coupling
├── fram:Variability
│   ├── fram:InternalVariability
│   ├── fram:ExternalVariability
│   ├── fram:UpstreamVariability
│   └── fram:DownstreamVariability
├── fram:VariabilityDimension
│   ├── fram:TimingDimension
│   ├── fram:DurationDimension
│   ├── fram:SequenceDimension
│   ├── fram:PrecisionDimension
│   ├── fram:ForceDimension
│   ├── fram:DistanceDimension
│   └── fram:DirectionDimension
├── fram:Phenotype
│   ├── fram:TimingPhenotype
│   ├── fram:DurationPhenotype
│   ├── fram:SequencePhenotype
│   ├── fram:PrecisionPhenotype
│   ├── fram:ForcePhenotype
│   ├── fram:DistancePhenotype
│   └── fram:DirectionPhenotype
├── fram:PerformanceCondition
├── fram:FunctionalResonance
├── fram:PhenotypeMappingRule
├── fram:WAIDeclaration
├── fram:EmergentPhenotypeResult
├── fram:WAIWADComparison
├── fram:Constant
├── fram:Variable
├── fram:OutputMessage
├── fram:InterpretationProfile
├── fram:FRAMScenario
├── fram:Passthrough
├── fram:ModelSummary                  ← NEW v1.8.0
├── fram:QuantitativeMetadata          ← NEW v1.8.0
├── fram:VariabilityPropagation        ← NEW v1.8.0
└── fram:FRAMPrinciple                 ← NEW v1.8.0

gUFO Alignment (rdfs:subClassOf — 15 axioms):
  fram:Function                → gufo:Event
  fram:HumanFunction           → gufo:Event
  fram:Aspect                  → gufo:IntrinsicMode
  fram:Coupling                → gufo:RelationalQuality
  fram:Variability             → gufo:Quality
  fram:VariabilityDimension    → gufo:Quality
  fram:PerformanceCondition    → gufo:Disposition
  fram:FunctionalResonance     → gufo:Event
  fram:Phenotype               → gufo:QualityValue
  fram:FRAMModel               → gufo:Object
  fram:FRAMScenario            → gufo:Object
  fram:PhenotypeMappingRule    → gufo:AbstractIndividual
  fram:WAIDeclaration          → gufo:AbstractIndividual
  fram:EmergentPhenotypeResult → gufo:Event
  fram:WAIWADComparison        → gufo:AbstractIndividual

// Enhanced Variability with Phenotypes
{
  "@type": "Variability",
  "potential": "Moderate",
  
  "hasPhenotype": [
    {
      "@type": "TimingPhenotype",
      "value": "OnTime",
      "probability": 0.70,
      "description": "Function completes within expected window"
    },
    {
      "@type": "TimingPhenotype",
      "value": "TooLate",
      "probability": 0.15,
      "description": "Delayed output — may affect downstream"
    },
    {
      "@type": "TimingPhenotype",
      "value": "TooEarly",
      "probability": 0.10,
      "description": "Premature output"
    },
    {
      "@type": "TimingPhenotype",
      "value": "NotAtAll",
      "probability": 0.05,
      "description": "No output — blocks downstream functions"
    }
  ],
  
  "hasContributingFactor": [
    {
      "@type": "PerformanceCondition",
      "name": "Available Time",
      "impact": "High",
      "description": "Tight schedules increase timing variability"
    }
  ]
}