Supply Chain’s AI Building Blocks: Knowledge Graphs vs Ontologies

At a recent Zero100 event on building fusion teams, several supply chain and operations leaders shared that, while they are navigating pitches for everything from "AI-powered ontologies" to "next-gen knowledge graphs," there’s some confusion about what these tools actually are, what they mean for AI and talent strategy, and how to sift the murky vendor landscape to pick the right tools for their own operations. 

Clarifying between them is crucial, not least because most sophisticated AI applications need both ontologies and knowledge graphs. The ontology ensures consistent reasoning; the knowledge graph provides rich, connected data that makes insights valuable. Reflecting on some of the takeaways from discussions with the community, here's the breakdown you need to start (or continue) your journey to build your data framework in the age of agentic.  

Ontology vs Knowledge Graphs: What Are They? 

An ontology is your supply chain's constitutional framework. It defines what exists in your domain and how those things can relate to each other. Think of it this way: your ontology might establish that a "Supplier" can have "Materials," those "Materials" must have "Quality Certifications," and those "Quality Certifications" expire. It creates shared vocabulary and eliminates the ambiguity that kills AI projects.  

Because ontologies are the structure around your operations and data rules, and not the data itself, they can be reusable. It’s like a blueprint for a house, defining structure and rules, but it’s not the house itself. Build one solid supplier ontology, and you can deploy it across procurement, risk management, and compliance systems. That's where the ROI lives. 

Continuing the analogy: if ontologies are the blueprint, knowledge graphs are the actual house, built from the blueprint and filled with real furniture, people, and activity. Knowledge graphs take your ontology rules and populate them with actual data. While your ontology says, "Suppliers can have Materials," your knowledge graph contains the specific fact that "ABC Metals supplies Grade-A titanium with AS9100 certification to Company X's Seattle facility." 

This is where supply chain visibility gets real and custom to your org. Knowledge graphs connect data across different stages, from raw materials to finished products, enabling companies to optimize logistics, predict disruptions, and identify bottlenecks before they become problems. For example, a knowledge graph might reveal that three seemingly unrelated suppliers share a common sub-tier vendor, creating hidden concentration risk. That's actionable intelligence your ERP system can't deliver. 

When to Use What 

While combining ontologies and knowledge graphs is powerful, some use cases lean more heavily on one than the other. Here are typical scenarios best suited to each:  

Ontologies: 

• Cross-company collaboration: Ontologies provide the foundation for information sharing across companies, industries, and stakeholders. This can include logistics processes and supply chain performance measures to ensure data is reported and collected consistently.  

• Regulatory compliance: Creating consistent frameworks for ESG reporting, tracking conflict minerals, and ensuring trade compliance. 

• System integration: Standardizing data exchange between ERP, procurement, and logistics platforms. 

Knowledge Graphs: 

• Supply chain visibility: Real-time mapping of suppliers, raw materials, products, and logistics to identify weak points and predict disruptions. 

• Risk management: Using neural networks to predict missing relationships and identify hidden supply chain risks. 

• Procurement optimization: Finding alternative suppliers, identifying cost savings opportunities, and optimizing sourcing strategies. 

The Hiring Market Reality 

Much like the broader development of AI and digital, the right talent is vital for implementation.

While hiring for skills to build and maintain ontology frameworks and knowledge graph remains limited (only 1% of companies we track are hiring for both), leaders like AstraZeneca, Amazon, BASF, and Bosch are seeking to embed both sets of skills in their operations.

Ontology roles focus on domain modeling and semantic standards and can show up in job titles like Ontology Architect, which was recently advertised for by Panasonic. Knowledge graph roles emphasize database implementation and AI/ML integration as well as semantic engineering (see this example from AstraZeneca for a Knowledge Graph and Semantic Engineer).  

Many organizations hire for both, but sequence the investments strategically. For example, medical services and pharma lead the way on hiring for ontology skills, likely due to the robust regulatory environment they operate in. Pharma is also a leader, alongside the auto industry, when it comes to knowledge graphs, allowing them to take advantage of an already established and strong data foundation.  

We’d suggest starting with ontology if you have major data integration challenges across systems, are planning multiple AI applications that need to work together, and/or you are in a regulated industry requiring consistent definitions. We’d suggest heading straight for knowledge graphs if you have specific, high-value use cases already (like supplier risk prediction), your data is already data relatively well-structured, and/or you need to demonstrate AI value quickly.  

And the hybrid approach, which is how most successful implementations start, is with a "minimum viable ontology" – just enough structure to support the first knowledge graph application, then expand as additional use cases emerge. 

Supply Chain Intelligence > Buzzwords 

The difference between ontologies and knowledge graphs isn't academic; it's key for successful AI implementation. Knowledge graphs following clear ontologies deliver better performance than unstructured approaches, therefore unlocking more valuable insights.  

Don’t let vendors conflate these technologies and turn them into hype terms to justify bigger contracts. As ontologies aren’t customized to who you are as an organization, they alone can’t create competitive advantage. Ask how they can be personalized to your operations. Or, better still, own that data yourself.  

Find out more on knowledge graphs in this Signal by Matt Davis, Chief Content Officer. And if you're interested in delving into this topic further, reach out to Cody Stack, VP, Data & Analytics, at Cody.Stack@zero100.com.