PropertyValue

PropertyValue is a Schema.org type that encapsulates a single property-value pair, extending the generic StructuredValue class. It is designed to describe characteristics that don't fit into Schema.org's predefined property hierarchy.

• Primary use case: Product specifications, custom attributes, technical parameters
• Parent type: Thing > Intangible > StructuredValue
• Key distinction: Unlike fixed Schema.org properties, PropertyValue allows arbitrary property naming via the propertyID or name fields

This type is essential for representing domain-specific attributes—such as "flux capacitance" or "thermal efficiency"—that lack dedicated Schema.org vocabulary.

The two foundational fields of PropertyValue are name and value, which together form the property-value pair.

• name (Text): The human-readable label of the property, such as "Processor" or "Max Load Capacity"
• value (Text, Number, Boolean, or StructuredValue): The actual data point associated with the property

Example JSON-LD:

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{
  "@type": "PropertyValue",
  "name": "Processor",
  "value": "Apple M3 Pro"
}

When value is numeric, AI parsers can perform comparative analysis across products. When it's textual, it serves as a descriptive attribute for generative summaries.

The propertyID field provides a machine-readable, globally unique identifier for the property being described, enabling unambiguous entity linking across knowledge graphs.

• Format: Typically a URL or IRI referencing an external vocabulary or standard
• Common sources: Wikidata property IDs (https://www.wikidata.org/wiki/Property:P2067), GS1 codes, or industry-specific ontologies
• Purpose: Disambiguates properties with similar names across different domains

Example:

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{
  "@type": "PropertyValue",
  "name": "Mass",
  "propertyID": "https://www.wikidata.org/wiki/Property:P2067",
  "value": "1.4"
}

This field is critical for entity salience optimization, as it allows AI engines to map your custom property to a canonical concept in their training data.

For numeric values, PropertyValue supports explicit unit specification through unitCode and unitText, transforming raw numbers into semantically rich measurements.

• unitCode (Text or URL): A machine-readable unit identifier using UN/CEFACT Common Codes or similar standards
• unitText (Text): A human-readable unit string, such as "kilograms" or "GHz"

Example with units:

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{
  "@type": "PropertyValue",
  "name": "Clock Speed",
  "value": "3.5",
  "unitCode": "GHZ",
  "unitText": "GHz"
}

This precision enables comparative reasoning by AI models—they can correctly interpret that 3.5 GHz is faster than 2.8 GHz without hallucinating incorrect unit conversions.

The valueReference property allows a PropertyValue to point to another Schema.org entity as its value, rather than using a literal string or number.

• Use cases: Linking to a DefinedTerm for enumerated values, referencing a QuantitativeValue for complex measurements, or pointing to an external standard
• Benefit: Creates rich, typed relationships within your knowledge graph instead of flat text

Example:

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{
  "@type": "PropertyValue",
  "name": "Energy Efficiency Class",
  "valueReference": {
    "@type": "DefinedTerm",
    "name": "A+++",
    "inDefinedTermSet": "EU Energy Label"
  }
}

This approach strengthens entity linking and ensures AI models understand that "A+++" is a defined classification, not arbitrary text.

PropertyValue is most commonly deployed as an item within the additionalProperty array of a Product or similar entity type.

• additionalProperty: A Schema.org property on Product, Service, and other types that accepts an array of PropertyValue objects
• Pattern: Each technical specification or custom attribute becomes a discrete PropertyValue node

Full product example:

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{
  "@type": "Product",
  "name": "Industrial Sensor X200",
  "additionalProperty": [
    {
      "@type": "PropertyValue",
      "name": "Sensing Range",
      "value": "10",
      "unitText": "meters"
    },
    {
      "@type": "PropertyValue",
      "name": "IP Rating",
      "value": "IP68"
    }
  ]
}

This structured approach enables AI overviews to extract and compare product specifications with high fidelity, directly supporting generative engine optimization strategies.