Creating a Scalable Grocer E-Commerce Knowledge Graph with GraphDB

Introduction

For large grocery e-commerce platforms like Europlenti, structuring product data efficiently is crucial for search accuracy, navigation, and personalization. A knowledge graph enables hierarchical product categorization, faceted search, and intelligent recommendations. In this blog post, we’ll explore how to build a scalable knowledge graph using GraphDB.

Why Use a Knowledge Graph for a Grocery E-Commerce Platform?

Grocery product catalogs are highly hierarchical and complex, requiring structured relationships between:

• Categories & Subcategories (e.g., Dairy → Milk → Skim Milk)
• Products & Attributes (e.g., Brand, Organic, Vegan, Gluten-Free)
• Supplier & Inventory Data (e.g., Available stock, warehouse location)
• Nutritional Information (e.g., Calories, Protein, Allergens)

A GraphDB knowledge graph allows for:

• Faceted Search & Filters – Enabling advanced filtering (e.g., “show gluten-free snacks under 200 calories”).
• Scalable Navigation – Seamless browsing through categories and subcategories.
• AI-Powered Recommendations – Suggesting complementary products based on user behavior.

Setting Up GraphDB for Europlenti

First, install GraphDB and create a new repository.

# Download and install GraphDB
wget https://download.ontotext.com/graphdb/GraphDB-Free-9.9.0.zip
unzip GraphDB-Free-9.9.0.zip
cd GraphDB-Free-9.9.0/bin
./graphdb

Access GraphDB’s web interface at http://localhost:7200 and create a new repository named europlenti_kg.

Defining the Grocery Product Ontology

Using RDF and OWL, define the schema for Europlenti’s grocery categories, attributes, and products.

@prefix ep: <http://europlenti.com/ontology#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

# Define Categories
ep:Dairy rdf:type rdfs:Class .
ep:Milk rdf:type rdfs:Class ; rdfs:subClassOf ep:Dairy .
ep:SkimMilk rdf:type rdfs:Class ; rdfs:subClassOf ep:Milk .

# Define Product Attributes
ep:hasBrand rdf:type rdf:Property ; rdfs:domain ep:Product ; rdfs:range rdfs:Literal .
ep:hasCalories rdf:type rdf:Property ; rdfs:domain ep:Product ; rdfs:range rdfs:Literal .

Ingesting Product Data

To load data into GraphDB, use SPARQL INSERT queries.

PREFIX ep: <http://europlenti.com/ontology#>
INSERT DATA {
  ep:Product123 rdf:type ep:SkimMilk ;
                ep:hasBrand "Organic Valley" ;
                ep:hasCalories "80" .
}

This adds a new product to the knowledge graph under the Skim Milk category.

Querying the Knowledge Graph

Now, let’s retrieve all dairy products using SPARQL.

PREFIX ep: <http://europlenti.com/ontology#>
SELECT ?product ?brand ?calories WHERE {
  ?product rdf:type ep:Dairy .
  ?product ep:hasBrand ?brand .
  ?product ep:hasCalories ?calories .
}

This query returns all Dairy category products with brand names and calorie counts.

Scaling the Knowledge Graph

To scale Europlenti’s knowledge graph, consider:

• Integrating supplier & warehouse data for real-time inventory tracking.
• Using AI models to auto-classify new products into categories.
• Enhancing faceted search with richer semantic relationships and synonyms.

Conclusion

GraphDB provides a scalable, flexible knowledge graph for Europlenti’s grocery e-commerce platform, enabling better search, faceted filtering, and recommendations.

Would you like a follow-up post on integrating machine learning with the knowledge graph? Let me know in the comments!