An ontology defines the vocabulary. But can we actually use it to answer business questions?
We’ve defined our SAP Commerce concepts and relationships. Now it’s time to bring them to life.
In this article, we move into the hands-on implementation of our knowledge graph using Python, RDFLib, RDF and SPARQL.
Using a synthetic Commerce scenario, we’ll connect Product P100 to its variants, customers, carts, orders, promotions and payment information.
More importantly, we’ll explore how a single SPARQL query can traverse these relationships and reveal connected business records.
This is where the experiment becomes tangible: moving from a conceptual ontology to a queryable knowledge graph.
Let’s build the graph, query the relationships and see what it reveals.
An ontology defines the vocabulary. But can we actually use it to answer business questions?We’ve defined our SAP Commerce concepts and relationships. Now it’s time to bring them to life.In this article, we move into the hands-on implementation of our knowledge graph using Python, RDFLib, RDF and SPARQL.Using a synthetic Commerce scenario, we’ll connect Product P100 to its variants, customers, carts, orders, promotions and payment information.More importantly, we’ll explore how a single SPARQL query can traverse these relationships and reveal connected business records.This is where the experiment becomes tangible: moving from a conceptual ontology to a queryable knowledge graph.Let’s build the graph, query the relationships and see what it reveals. Read More Technology Blog Posts by SAP articles
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