What is a knowledge graph in fashion?
A knowledge graph is a network of connected data that represents things such as products, materials, suppliers and customers and the relationships between them.
In short
A knowledge graph is a way of organising data as a network of entities and their relationships. In fashion it can link styles, materials, suppliers, factories, certifications, collections and customers, making it easier to answer complex questions and to provide AI assistants with accurate, connected information.
How does it work in practice?
Data is stored as nodes, representing things, and edges, representing relationships. A node for a jacket might connect to its fabric, which connects to a mill, which connects to a certification and a country. The structure is usually guided by an ontology that defines what types of entities and relationships are allowed.
Fashion examples include:
- Product and material graphs linking styles, components, trims and suppliers.
- Traceability graphs following materials across supplier tiers for a Digital Product Passport.
- Style and trend graphs connecting attributes, occasions and complementary items.
- Customer and account graphs showing which retailers carry which brands and categories.
Why does it matter for fashion businesses?
Fashion data is spread across PLM, PIM, ERP and supplier systems, and many important questions cross these boundaries. Which styles use a fabric from a supplier under review? Which retail accounts stock products with a specific certification? A knowledge graph makes such questions answerable without manual research across spreadsheets.
How does AI use it?
Knowledge graphs can ground language models in verified facts. In a graph RAG setup, an assistant retrieves connected facts from the graph before answering, which reduces hallucinations and allows it to follow relationships across several steps. Machine learning can also help build graphs by extracting entities and links from documents.
Common pitfalls
- Starting too broad instead of with one clear business question.
- Poor master data, with duplicate suppliers or inconsistent material names.
- No ownership for keeping the graph up to date.
- Over-complex models that only specialists can maintain.
Frequently asked questions
What is the difference between a knowledge graph and a database?
A traditional database stores data in tables, while a knowledge graph emphasises relationships between entities. Graphs make it easier to query multi-step connections, such as product to material to supplier to certification.
How do knowledge graphs help with sustainability data?
They connect products to materials, suppliers and certifications across the supply chain, which supports traceability, reporting and product passport requirements.