In the era of Industry 4.0, knowledge graphs have emerged as a critical technology for modernizing data management and driving innovation. Knowledge graphs have evolved beyond their traditional role in data integration. Today, they’re central to automating processes, shortening decision-making cycles, and maximizing the value of data.

The reason for this growth is simple: knowledge graphs harmonize access to diverse data sources so both humans and other machines can understand it. In an increasingly complex digital landscape, traditional systems often fall short. Thankfully, knowledge graphs are rising to meet the demands of Industry 4.0.

Before we look at why knowledge graphs are gaining traction in Industry 4.0, it’s worth noting some key requirements for a knowledge graph stack. It must have the ability to:

1. Obstruct an ontology or semantic model, which describes the entities and relationships existing in the system.
2. Integrate data from diverse sources into a resource description framework (RDF)-based knowledge graph in alignment with the ontology.
3. Execute performant queries on the data.

With these requirements in mind, let’s explore how knowledge graphs are uniquely positioned in Industry 4.0 through three use cases. 

Why Knowledge Graphs Are Gaining Traction in Industry 4.0

Traditional data models struggle to capture modern manufacturing systems’ interconnectedness. Knowledge graphs, by representing data as a web of relationships, provide a more holistic way to integrate and analyze information. From optimizing digital twins to enhancing supply chain management and artificial intelligence (AI) capabilities, knowledge graphs are transforming how manufacturers process and interact with data, enabling more informed, more efficient decision-making.

Complex Manufacturing Systems and Processes

In manufacturing, systems are intricate and contain relationships that go far beyond simple tabular data. For example, digital twins represent real-time, data-driven models of physical assets, capturing their interactions and entire life cycle. These models require data from multiple sources to be cohesively integrated and analyzed. Knowledge graphs model these complex, interconnected relationships naturally.

A great example of this is the bill of materials (BOM) in manufacturing. The BOM describes the components and subassemblies that make up a product. Traditional relational databases struggle to model this hierarchical relationship effectively. By contrast, knowledge graphs can capture the relationships between entities—components, parts, and sub-assemblies—without the limitations of relational models.

Similarly, supply chain management can be effectively modeled as a network or graph, where entities (e.g., suppliers, manufacturers, distributors) are interconnected in dynamic ways. Knowledge graphs provide the flexibility to model these emergent and divergent relationships, making them an ideal fit for supply chain systems in Industry 4.0.

Semantic Reasoning Augments AI Capabilities

Traditional AI and machine learning models rely on vast amounts of data, but increasingly researchers are recognizing that knowledge graphs can augment these systems. By incorporating semantic reasoning—the ability to apply logic and contextualized data—knowledge graphs provide a deeper layer of intelligence. 

For example, Amazon is using semantic reasoning to optimize the movement of autonomous robots in fulfillment centers. By understanding the relationships between parts of the warehouse and the items they contain, these robots can move faster and more precisely.

The ability to apply reasoning within a knowledge graph also enables tasks like graph-based neural networks, which improve machine learning model accuracy. In Industry 4.0, this could have significant applications in predictive maintenance, quality control, and even robotics.

Enhanced Querying and Interaction

One of the most exciting advancements in knowledge graphs is their ability to transform how people interact with data. With the rise of large language models (LLMs), knowledge graphs are enabling new forms of Q&A engagement within enterprise systems. LLMs integrate a natural language interface to data within knowledge graphs, allowing users to ask complex questions and receive accurate answers in real time.

For example, using an ontology defined within a knowledge graph, a user could ask: “What are the components that occur more than twice in the bill of materials for tubes with sensors that have a temperature reading over 140?” 

The system would automatically generate a SPARQL query (a standard query language for RDF data) that returns the relevant results, even if the question involves multiple relationships or aggregations. Based on the first query’s results, users can ask relevant follow-up questions based on the context of the conversation. This capability can be a game-changer for manufacturers, enabling real-time data access and empowering operators, managers, and technicians to make faster, more informed decisions.

The Future of Knowledge Graphs in Industry 4.0

While many companies are still in the early stages of developing a knowledge graph strategy, early adopters are already seeing significant benefits. For example, a leading automotive company has made substantial investments in knowledge graph and semantic technologies. Altair’s full stack knowledge graph platform, Altair® Graph Studio™, is helping drive this transformation. 

Graph Studio can overlay and combine data from any source – structured and unstructured alike – into a unified knowledge graph with massively parallel processing (MPP). Each data source is loaded into the RDF graph engine as an in-memory layer, upon which additional layers can be applied to logically connect, extend, and transform the knowledge graph. This flexible, high-performance architecture enables organizations to seamlessly integrate and analyze complex, disparate data in real time, unlocking powerful Industry 4.0 insights and driving informed decision-making at scale.

Three key use cases where knowledge graphs make substantial impact in Industry 4.0 include: 

• Complex Manufacturing Systems: Graph Studio’s Graph Data Interface (GDI) enables direct parallel loading of data from enterprise sources into RDF, allowing in-memory transformations. This approach can handle any data shape, complexity, and quality. This allows the data to be loaded and then cleaned and restructured with powerful transformation queries. This eliminates the need for predefined schemas and rigid pipelines required by label property graph (LPG) databases. The result is greater agility with complex data, faster decision-making, improved cost and time efficiency, and simplified data integration.
• Semantic Reasoning: Semantic reasoning is computationally intensive and often requires multiple iterations. Graph Studio simplifies this process by streamlining the testing of different modeling and connection permutations in the knowledge graph, accelerating the generation of valuable AI inputs. Additionally, Graph Studio provides pre-built algorithms and a framework for data scientists to implement custom extensions (UDXs), leveraging the MPP performance of the underlying graph engine. This allows faster AI model development, scalability and customization, and adaptability to complex systems. 
• Advanced Q&A Engagement: Enterprise leaders often ask complex, resource-intensive questions. Graph Studio’s distributed OLAP architecture enhances the execution of these queries by creating a single in-memory database layer, eliminating the need for network load management and caching in data visualization. This enables fast, real-time answers for end users without putting strain on downstream systems.

Conclusion: Embracing the Future with Knowledge Graphs

The integration of knowledge graphs into the fabric of Industry 4.0 is more than just a passing trend – it’s reshaping how businesses make decisions, automate processes, and harness the power of data. As these systems evolve, they’ll continue to drive efficiency, enhance AI capabilities, and provide a semantic layer that makes data more accessible and more actionable.

Knowledge graphs lie at the heart of data’s future. Click here if you’re interested in learning how knowledge graphs can enhance your daily operations.