Build a Product Knowledge Graph for a Fast-Fashion Retailer
Overview
What this challenge is about.
Design an OWL ontology for 200 SKUs across 4 markets, model in RDF, run 8 SPARQL queries, and earn a verifiable certificate.
The scenario
The retailer (~165,000 staff, ~EUR 35bn revenue) is moving its data stack toward graph-based product modeling to support cross-market personalization at scale.
The Brief
What you'll do, and what you'll demonstrate.
Unify product attributes across 4 country catalogs into a single queryable knowledge graph with harmonized vocabulary.
Earning criteria — what you'll demonstrate
- Design an OWL ontology for a real product domain
- Model heterogeneous catalog data in RDF
- Author SPARQL queries that demonstrate harmonization
- Communicate KG design choices in a written specification
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Knowledge Graphs
Apply knowledge graphs to solve real industry problems and demonstrate production-level capability.
- Owl Ontology
Apply owl ontology to solve real industry problems and demonstrate production-level capability.
- Rdf
Apply rdf to solve real industry problems and demonstrate production-level capability.
- Sparql
Apply sparql to solve real industry problems and demonstrate production-level capability.
- Schema Design
Apply schema design to solve real industry problems and demonstrate production-level capability.
- Data Harmonization
Apply data harmonization to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Engineer
Building a product KG with harmonized vocabularies is core data-engineering work at any large retailer or marketplace.
This challenge sharpens
- knowledge-graphs
- rdf
- data-harmonization
AI Solutions Architect
Specifying the KG layer that AI features (search, recs, RAG) consume is the AI solutions architect's daily output.
This challenge sharpens
- schema-design
- knowledge-graphs
- data-harmonization
AI Engineer
Loading RDF and writing SPARQL queries to power downstream AI features is the AI-engineer skillset at any KG-driven product.
This challenge sharpens
- rdf
- sparql
- knowledge-graphs