What is Semantic model?
A semantic model is a structured framework that defines the meaning (semantics) of data by establishing clear relationships between concepts, entities, and their attributes — often using standardized vocabularies and ontologies. In simpler terms, it helps machines and people understand, interpret, and exchange data consistently, regardless of system or language differences.
Chapters
Semantic model in plain English
A semantic model:
- Uses standard terms (like “material”, “origin”, “carbon footprint”) with clearly defined meanings.
- Defines relationships between data points (e.g., “Product A is made of Material B, which has a carbon intensity of X”).
- Can be implemented using ontologies (like RDF, OWL) and data standards (like schema.org, ECLASS, or GS1).
Why is a semantic model important for Digital Product Passports (DPPs)?
Digital Product Passports are meant to store, share, and validate structured data about products across the supply chain and lifecycle. Here's why semantic models are critical:
1. Interoperability across systems
Semantic models enable different software systems and platforms to "speak the same language", making it easier to share data between manufacturers, suppliers, regulators, and consumers.
Without a semantic model, data might be misunderstood, mismatched, or unreadable across different stakeholders or technologies.
2. Regulatory compliance
EU regulations (like the Ecodesign for Sustainable Products Regulation – ESPR) increasingly mandate machine-readable, structured data. Semantic models ensure that DPPs are compliant and verifiable by authorities or platforms.
3. Data integrity and traceability
Semantic models define the context and relationships of data — making DPPs more reliable for tracking origin, composition, repairs, and recycling across the product lifecycle.
4. Automation and AI-readiness
With clear semantics, machines can automatically process, validate, and enrich DPPs — essential for smart manufacturing, predictive maintenance, and environmental analytics.
5. Sustainability and circular economy
A well-structured semantic model allows stakeholders to understand reuse, recycling, or carbon impact pathways by querying data consistently.
Example
Imagine a DPP says:material: PET — that might mean different things to different systems.
But with a semantic model:material: https://example.org/ontology#PET_0001
→ All systems understand it's Polyethylene Terephthalate, a recyclable plastic, with certain properties.
Summary
A semantic model is essential for DPPs because it provides the shared language and logic needed to make product data accurate, interoperable, machine-readable, and regulatory compliant. It's the foundation that turns product data into trusted, actionable knowledge for the circular economy.
Let me know if you want a visual diagram or real-world examples of semantic models used in DPPs (like Catena-X, GS1 Digital Link, or the ECLASS standard).