OpenEPCIS DPP Extensions
Browsable documentation and downloadable artefacts for 12 OpenEPCIS Digital Product Passport extension vocabularies. We ingest the upstream standards in whatever form they publish, normalize and cross-align them with a local open-source AI pipeline, and serve the result here in every format.
Masterdata — GS1 Web Vocabulary
The upstream vocabulary the masterdata engine models against — Products, Places, and Organizations. Not an EPCIS extension; anchored separately.
How this reference is built
From scattered upstream vocabularies to one consistent, machine-readable reference.
1 · Ingest
Pull each upstream vocabulary, whatever depth it ships in, and re-publish it in every format: JSON-LD context, JSON Schema, ontology TTL and SHACL.
2 · Normalize
A local, open-source LLM pipeline aligns our terms to the upstream vocabularies by meaning, and records a QA confidence for every mapping.
3 · Publish & browse
Everything is generated from the canonical TTL and served here: browsable, searchable, deep-linked, and downloadable in all four formats.
Extension modules
12 regulation-aligned vocabularies. Open one to browse its classes, properties and alignments.
DPP Core
Cross-jurisdictional core vocabulary for Digital Product Passports (v0.9.7). ESPR-compliant.
Battery DPP
Vocabulary for EU Battery Regulation (2023/1542) Digital Product Passports (v0.9.7). Covers battery chemistry.
EUDR
Vocabulary for EU Deforestation Regulation (2023/1115) compliance (v0.9.7). Covers geolocation.
Textile DPP
Vocabulary for EU Sustainable Textiles Strategy and ESPR (v0.9.7). Covers fiber composition.
Electronics DPP
Vocabulary for ESPR Electronics delegated acts (v0.9.7). Covers French Repairability Index.
Detergent DPP
Vocabulary for EU Detergents Regulation 2026/405 (v0.9.7). Covers INCI ingredients.
PPWR DPP
Vocabulary for the EU Packaging and Packaging Waste Regulation 2025/40 (v0.9.7). Intentionally thin — only packaging-specific concepts (Packaging.
CPR DPP
Vocabulary for the revised EU Construction Products Regulation 2024/3110 (v0.9.7). Intentionally thin — only construction-specific concepts (ConstructionProduct.
Iron & Steel DPP
Vocabulary for the iron & steel ESPR priority product group.
FSMA 204
Vocabulary for the U.S. FDA Food Safety Modernization Act §204 Final Rule on Food Traceability.
GS1 Rail
GS1 Rail Vocabulary v1.6 — sectoral Layer-1 vocabulary published by GS1 AISBL with GS1 Switzerland. Covers WTMS sensor metadata.
CIRPASS-2 EUDPP Core Ontology
EUDPP Core Ontology v2.0.5 — the CIRPASS-2 pilot programme's EU DPP data model (Semantic Treehouse / TalTech).
Every format, for the best precision
Upstream vocabularies differ in depth. We level them up so you never lose semantics.
Some upstream standards publish a full OWL/Turtle ontology; others ship only a JSON-LD @context, and a few are little more than a code list. Rather than inherit those gaps, every OpenEPCIS module is published in all four artefacts, each doing a different job:
JSON-LD context
Maps your JSON keys to stable IRIs so a plain document becomes Linked Data.
JSON Schema
Validates shape and data types for everyday API and integration tooling.
Ontology (OWL/TTL)
The canonical source: classes, properties, hierarchy and cross-vocabulary alignment.
SHACL shapes
Enforces regulatory constraints (required fields, cardinalities, value ranges).
Every term URI also supports content negotiation, so the same address serves the format you ask for:
# Turtle / OWL
curl -H "Accept: text/turtle" https://ref.openepcis.io/extensions/eu/battery/
# JSON-LD context
curl -H "Accept: application/ld+json" https://ref.openepcis.io/extensions/eu/battery/Normalized with a local, open-source AI pipeline
How thousands of cross-vocabulary alignments are proposed and checked.
Connecting our terms to the upstream vocabularies (GS1, the EU SEMIC Core Vocabularies, schema.org, UNTP, DPP Keystone) is thousands of fine-grained judgement calls. We automate most of it with a small command-line pipeline built on Quarkus and LangChain4j that runs entirely on local hardware with open models, so nothing leaves the machine and a full run costs nothing.
- Read both sides — our ontology and each upstream vocabulary are loaded into a common term model.
- Match by meaning — text embeddings narrow millions of possible pairs down to the plausible ones.
- Grade each pair — a language model proposes the precise relationship (same as, broader than, narrower than, related, or no match).
- Double-check — a second, stronger model re-judges each proposal as a blind panel and records a confidence; a person signs off before anything is published.
The mappings are stored as standard graded SKOS in the ontology; the models and confidence behind each one are kept as an auditable trail.
Browse, search, and trust the mappings
What the processed vocabularies turn into on this site.
- Navigate by region and regulation module down to each class and property, with its definition, domain/range, and where it is used.
- Search across every term, example and reference vocabulary from one box.
- Cross-vocabulary alignment on each term, with deep links straight to the matching term in GS1, schema.org, SEMIC, UNTP or DPP Keystone.
- QA confidence shown on each AI-graded mapping (e.g. “· QA 92%”), with the bulk and verifier models named, so you can see how every alignment was decided.