ecommerce_ops · workflow
Zalando builds a fashion knowledge graph with semantic web technologies to enable implicit concept search and vegan-aware filtering
Zalando's product data lacked the background knowledge to express implicit fashion concepts — for example, that the absence of wool, silk, and leather equals 'vegan' — making it impossible to answer customer queries like 'vegan clothes' from product attributes alone.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Expressivity gap identified
The Helsinki office asked how implicit fashion attributes like 'vegan' or 'sustainable' could be derived from raw product data.
Tools used
TurtleJSON-LDPython
Outcome
The fashion knowledge graph enables Zalando to understand 'vegan' in search without it appearing in product data, offer it as a filter value, and show knowledge pages combining articles, outfits, and vegan-appropriate products from the catalogue — and to apply business rules such as suppressing competing brand suggestions.
Grounding & classification
Source type: technical build writeup
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enterprise searchknowledge searchknowledge baseproduct catalognamed customertools describedworkflow describedecommerceretailtechnical build writeupecommerce opsdata sync enrichment