Then came the introduction of a Knowledge Graph—and with it, a new way to navigate data, collaborate with colleagues, and get to the root of challenges faster. Below, we’ll walk through what this transformation looks like, focusing on the technical underpinnings, real-world use cases, and practical tips to make Knowledge Graphs a reality in your own organization.
Graph thinking in action: A morning of quick problem-solving
Sara starts her day with a notification: a potential issue has been flagged on the latest engine control unit (ECU) software update. Previously, she would have opened multiple spreadsheets and rummaged through emails to verify which cable harnesses or sensors might be impacted. Now, with a Knowledge Graph in place, she simply logs into a unified dashboard:
- A single query—phrased in everyday language like “Show me all components affected by ECU software version 3.2.1”—triggers an underlying GraphQL request.
- Instantly, the system reveals a network of impacted parts, suppliers, and related software modules. Sara sees that a particular harness in the cooling subsystem may be at risk, along with an outdated calibration file.
This immediate visual of nodes (parts, documents, software versions) and edges (dependencies, relationships, version links) saves her precious hours. Rather than performing manual cross-referencing, she has an interactive map of where issues might cascade across her design.
The technical backbone of the Knowledge Graph
It’s easy to get lost in buzzwords like “AI” or “Big Data.” But a Knowledge Graph is grounded in tangible, proven technologies that unify your data. In Sara’s company, this happens through systematic ingestion and integration, carefully designed schemas/ontologies, and flexible query mechanisms—all powered by a graph storage layer that makes multi-hop traversals and real-time insights possible.
Here is how it works:
1) Data Ingestion & Integration via Connectors for PLM, ERP, and QMS
Automated pipelines extract data from each source—such as CAD drawings, vendor lists, or testing reports—and transform them into node-edge relationships. This eliminates the need for manual cross-referencing or ad hoc spreadsheet merges.
- Schema or Ontology
A consistent schema or ontology ensures that “engine_cooling_unit” in one system is recognized as the same entity as “CU-Eng” in another. By setting clear conventions, engineers like Sara no longer waste time deciphering inconsistent field names across databases.
2) Query mechanisms
GraphQL Let’s front-end applications (or Low-Code platforms) request exactly the data they need, avoiding the heavy lifting of extensive joins or over-fetching.
Engineers can type queries using natural language—“Which parts are affected by the new ECU software update?”—and the system automatically translates that into structured graph queries behind the scenes.
(In other setups, SPARQL, Cypher, or Gremlin might be used directly, depending on whether the organization prefers an RDF or property-graph model.)
3) Storage options
Sara’s organization chose a property graph database (with Cypher-like queries) because it fit their existing development tools and team expertise. However, a Resource Description Framework (RDF) with SPARQL remains a strong option if your use cases demand tighter semantic web standards.
- Performance & Scalability
Graph databases specialize in multi-hop traversals—quickly revealing how a single design change ripples through the system. Indexing strategies and, if necessary, distributed storage (sharding) ensure the graph remains efficient under large data volumes.
4) Key technical considerations
When working with Knowledge Graphs, it’s crucial to consider several technical aspects to ensure optimal performance and usability. These considerations include the way data is structured and modeled, how data is processed and governed, and how users interact with the graph through queries and analysis.
- Data modeling: The choice of graph type and schema structure determines how well the graph represents relationships and supports business requirements.
- RDF vs. Property Graphs: RDF uses triples (subject-predicate-object) with SPARQL; property graphs store node/edge properties and often use Cypher or Gremlin.
- Schema Design: Determine how detailed or flexible your ontology or property definitions should be.
- ETL & Governance: These processes determine how raw data is converted into meaningful knowledge within the graph while keeping it trustworthy and compliant.
- ETL Pipelines: Transform tabular or document-based data into graph form, maintaining quality checks.
- Versioning & Security: Fine-grained access controls and update processes keep sensitive data protected and ensure that changes are properly audited.
- Query & Traversal: How users interact with the graph is critical to unlocking its potential.
- Multi-hop traversals let you see, for instance, how one component’s failure might impact a sub-assembly or trigger compliance requirements.
- GraphQL or natural-language wrappers simplify the interface for non-technical users.
How engineers benefit from graph technology
Before the Knowledge Graph rollout, version control was a nightmare. Sara would discover new parts tested against old manufacturing specs, leading to rework and wasted time. Meanwhile, in the supply chain, incorrect part numbers could linger for weeks before someone caught the discrepancy. The result? Delayed production schedules, confusion among teams, and higher costs.
By combining data ingestion & integration with clear schemas/ontologies and leveraging graph-based storage and query mechanisms, Sara’s company bridges siloed systems into a coherent, searchable platform.
The result? Engineers spend less time rummaging through spreadsheets and more time solving critical design challenges, innovating faster, and ensuring product quality across the board. Every entity—be it a part, a software revision, or a vendor—has a single source of truth in the graph.
How graph thinking is already used in other industries
Over lunch, Sara scrolls through industry news. She comes across familiar Knowledge Graph examples:
LinkedIn’s recommendations
Job recommendations or “People You May Know” rely on graph relationships between skill sets, companies, and professional connections. Seeing large consumer tech successes reminds Sara how powerful the same concept can be for her organization’s challenges—whether it’s engineering changes or supply chain complexity.
Netflix’s recommendation system
Although they might not always label it as a Knowledge Graph, Netflix’s recommendation system leverages “graph thinking” to link users, content genres, and viewing behaviors. By modeling these relationships, Netflix delivers spot-on suggestions—saving viewers time and boosting engagement.
Google Maps
Google’s approach goes well beyond plotting points. Each place—restaurants, museums, repair shops—has relationships to reviews, opening hours, and real-time traffic. This interconnected “mesh” of information forms a specialized graph that helps people find exactly what they need in context.
The afternoon deep dive: AI and the Knowledge Graph
Sara has been testing a Predictive Maintenance model to forecast failures in the cooling subsystem. She’s proud of the machine learning approach but sometimes wonders if the model overlooks certain complexities. Enter the Knowledge Graph:
Structured context for AI
The graph stores explicit relationships (e.g., “Part A depends on Part B”). The AI model can pull only the relevant subgraph, reducing noise and improving predictions.
Explainable results
When the model recommends early replacement of a sensor, it points to the graph path: it discovered a pattern among sensors from the same batch with higher failure rates under similar conditions. This traceability reassures her team about the model’s validity.
No, AI does not require a graph for everything
Sara acknowledges that advanced deep-learning systems can function without a formal Knowledge Graph. However, the synergy is what makes her job easier—structured data unlocks a deeper, more transparent form of AI-driven analysis.
From pilot to production: Challenges and best practices
Even with a supportive leadership team, the journey wasn’t always smooth. Sara’s department faced:
Data quality problems
Outdated or missing part references carried over from legacy systems. Best Practice: They now run frequent data quality checks and rely on domain experts to fix inconsistencies.
Avoiding overly complex ontologies
Initially, her team tried to define an all-encompassing ontology of every conceivable component. It quickly became unwieldy. Best Practice: Focus on critical domains first (e.g., engine components, harnesses) before branching out.
Performance under-load
Some queries spanned large subgraphs, slowing response times. Best Practice: They added appropriate indexing strategies, caching, and carefully planned their graph model for typical query patterns.
Collaboration across departments
The biggest operational shift? Encouraging mechanical engineers, software teams, and supply chain managers to contribute to the same knowledge structure. Best Practice: Sara’s company formed a cross-functional task force that meets monthly to align on data definitions and updates.
Driving real value: Use Cases in Automotive & Aerospace
Rapid root-cause analysis
When a fault code emerges, the graph reveals direct and indirect relationships—sensor signals, software versions, and previously recorded incidents. Engineers can trace possible causes in hours instead of days.
Compliance & Traceability
Regulatory bodies often require detailed documentation on where every part originated and how it was tested. With a unified graph, Sara’s team can produce a complete audit trail—which supplier delivered which batch, tested under which standards—all in a single query.
Generative Design
Working on new car interior designs? A Knowledge Graph helps feed AI algorithms with constraints such as safety standards, materials, part compatibility, and cost. The AI can propose multiple validated design options faster, leaving Sara more time for creativity and innovation.
Wrapping up the workday
By late afternoon, Sara finalizes her fix for the ECU software update. Thanks to the graph-backed system:
- She wrote no complex code—her company’s Low-Code environment allowed a drag-and-drop approach to visualize the graph.
- She typed a few plain-language queries to confirm dependencies, letting the system handle the intricacies of GraphQL behind the scenes.
- She used an interactive dashboard to see real-time data from all relevant sources, including test logs, part inventories, and historical repair tickets.
What used to take days now took a matter of hours, freeing her to think more strategically about future design improvements.
Final thoughts
For Sara and many other engineers in automotive and aerospace, Knowledge Graphs provide more than just a fancy data structure, they make it possible to:
- Stop wasting time searching in siloed systems.
- Collaborate effectively with cross-disciplinary teams.
- Implement AI that actually understands (and explains) the relationships within a product’s ecosystem.
Ultimately, Knowledge Graphs return hours of creative energy to engineers, letting them focus on innovation instead of bureaucratic “data-hunting.” If you’re considering a similar transformation, remember to start small, prioritize data governance, and keep the interface engineer-friendly.
When executed well, a Knowledge Graph can transform not just data pipelines but the very culture of engineering—and that might be the biggest game-changer of all.
We at SPREAD believe that the increasing product complexity and lack of full system understanding and “product sovereignty” engineers grapple with is an industry-wide issue, now starting to gain public recognition. We would be excited to partner with you in accelerating the industry-wide digital transformation, driving a data-driven approach to R&D Management and steering in manufacturing industries.
Leveraging our deep product engineering expertise, AI capabilities, and proven knowledge graph technology, we have developed a solution to help engineers easily identify dependencies in complex systems like aircrafts, maintaining a comprehensive overview of functionality and maturity amid rising complexity.
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