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 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.
Knowledge Graphs and Semantic Web
Master · Ai Ml
Fit score: 1
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career paths this builds toward
Canonical rolesData 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