What is an industrial knowledge graph (Industrial Data Fabric)?
An industrial knowledge graph is a graph-based data structure that represents the relationships between operational entities – such as equipment, processes, materials, and personnel – across manufacturing environments. This represents a fundamental shift in how manufacturing organizations structure, connect, and leverage their operational data to drive intelligent decision-making across complex production ecosystems.
Defining Industrial Knowledge Graphs
Knowledge graphs (KGs) are machine-readable data structures that represent knowledge of the physical and digital worlds, including entities and their relationships, which adhere to a graph data model. [1] In industrial contexts, these sophisticated data architectures transcend traditional database limitations by creating semantic connections between disparate manufacturing elements—from equipment hierarchies and process flows to quality parameters and maintenance histories.
The industrial knowledge graph (IKG) can improve the cognitive intelligence of the manufacturing system and is recognized as one of the cores of the next-generation industrial management information system. [2] Unlike relational databases that store information in rigid, predefined schemas, industrial knowledge graphs model the dynamic, interconnected nature of modern manufacturing operations.
Technical Architecture
Industrial knowledge graphs consist of three foundational components:
- Entities: Manufacturing assets (machines, sensors, components), processes, materials, personnel, and operational parameters
- Relationships: Semantic connections defining dependencies, hierarchies, workflows, causality, and historical interactions
- Attributes: Contextual metadata providing deeper insight into each entity and relationship (such as timestamps, specifications, conditions,and statuses)
This architecture enables manufacturers to query complex relationships across operational domains. For example, determining which supplier quality issues correlate with specific equipment performance degradation patterns, or identifying which process parameter combinations optimize both energy consumption and product quality.
Advantages Over Traditional Data Systems
| Traditional Relational Databases | Industrial Knowledge Graphs |
| Rigid, predefined schema | Flexible, extensible schema adaptable to changing requirements |
| Limited relationship modeling capabilities | Native support for complex, multi-dimensional relationships |
| Siloed data across departmental systems | Unified, contextualized view across OT, IT, and engineering domains |
| Query complexity increases exponentially | Intuitive traversal of connected data relationships |
| Difficult integration of heterogeneous sources | Seamless incorporation of diverse data formats |
The limitations of relational databases are no longer tenable in today’s manufacturing ecosystem. A knowledge graph structure, on the other hand, addresses these limitations by placing data in context by creating links and metadata, providing a structure for the integration and unification of the contained data. [3]
Strategic Applications in Manufacturing
Predictive Asset Intelligence
A knowledge graph can dynamically create an information network that represents all the semantic and other relationships in the technical documents and data. For example, using the knowledge graph, the agent would be able to determine a sensor that is failing was mentioned in a specific procedure that was used to solve an issue in the past. Once the knowledge graph is created, a user interface allows engineers to query the knowledge graph and identify solutions for particular issues. The system can be set up to collect feedback from engineers on whether the information was relevant, which allows the AI to self-learn and improve performance over time. [4]
Manufacturing Process Optimization
Research demonstrates significant operational improvements through industrial knowledge graph implementation. In an enterprise’s aero engine blade production line, our designed system was validated over a period of 5 months (2024), covering a total of 200 batches of blade products. During the validation period, the maximum contour error precision of the blades gradually decreased from 0.073mm in January to 0.062mm in May. This result indicates that our designed system significantly improved the processing precision of the blade contours during production, reducing the error further. At the same time, the product qualification rate increased from 81.3% in January to 85.2% in May. [5]
Knowledge Extraction and Reuse
In many industrial manufacturing processes, human operators play a central role when it comes to parameterizing the involved machinery and dealing with errors in the process. However, large parts of the acquired process knowledge are tacit, leading to difficulties sharing the knowledge between operators. Therefore, knowledge extraction is a necessary but time and cost intensive process, requiring both specially trained personnel and experienced operators. In contrast, we propose that by gathering insights into what influenced operators’ actual parameter choices, tacit process knowledge can be extracted during production in an example-based manner.
Industry 4.0 Integration and Interoperability
Achieving interoperability is a crucial factor for realizing the Industry 4.0 (I4.0) vision. In I4.0 different systems exist, and the demand for the creation of an integrated view using the existing data increases. However, interoperability between data sources is hampered due to different representation of similar processes or production parts. In this paper, we present a knowledge graph based approach for semantically integrating data sources in I4.0 scenarios. [6]
Market Momentum and Industry Adoption
Gartner Recognition and Strategic Importance
Gartner’s 2024 report places knowledge graphs on the “Slope of Enlightenment,” highlighting their increasing maturity and essential role in enterprise AI strategies. This recognition underscores a pivotal shift in the industry as more life sciences organizations realize the transformative potential of knowledge graphs, particularly when integrated with AI and machine learning (ML). [7]
According to Gartner, 80% of data and analytics innovations will use graph technologies by 2025. [8] This statistic reflects the growing recognition of knowledge graphs as foundational infrastructure for advanced manufacturing analytics.
High-Performance Manufacturing Results
Respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to gen AI and related natural-language capabilities. [9] This correlation between knowledge graph adoption and organizational performance demonstrates the competitive advantages these technologies provide.
Manufacturing leaders are increasingly recognizing that there’s one significant asset that manufacturers have not yet optimized: their own data. Process industries generate enormous volumes of data, but many have failed to make use of this mountain of potential intelligence. [10]
SymphonyAI’s Industrial Leadership
SymphonyAI has established itself as a leading provider of industrial knowledge graph solutions, delivering measurable value through its IRIS Foundry platform. The company’s comprehensive approach addresses the unique challenges of manufacturing environments while providing enterprise-grade capabilities that scale across global operations.
SymphonyAI’s industrial knowledge graph platform combines advanced AI capabilities with deep manufacturing domain expertise, enabling organizations to transform their operational data into strategic competitive advantages.
Implementation Considerations
Data Architecture Requirements
Smart manufacturing (SM) confronts several challenges inherently suited to knowledge graphs (KGs) capabilities. The first key challenge lies in the synthesis of complex and varied data surrounding the manufacturing context, which demands advanced semantic analysis and inference capabilities. The second main limitation is the contextualization of manufacturing systems and the exploitation of manufacturing domain knowledge, which requires a dynamic and holistic representation of knowledge. [11]
Technical Challenges and Solutions
Given that the numerous data embedded in manufacturing processes and products are separated, it is challenging to tackle and integrate heterogeneous data in industrial scenarios. In this context, an industrial knowledge graph (iKG) has been developed as a promising semantic organisation to leverage the rich information from multiple resources. However, relations are usually missing and hidden in original iKGs, which results in the necessity for iKG completion. Given these two perspectives, a framework of iKG construction is proposed based on ontology and link prediction in this study. [12]
Future Outlook and Strategic Implications
As a result of the development of a new generation of artificial intelligence and carbon-neutral technologies, traditional industries are undergoing dramatic transformations. The exploration of industrial intelligence is still in its nascent stages, particularly lacking technical approaches to distill experiential knowledge from heterogeneous data sources originating from various origins. Knowledge Graphs (KG), as cutting-edge artificial intelligence technologies, can enable knowledge management and reuse while condensing valuable knowledge. As a result, fully utilizing KG’s potential in the industrial field is critical to the realization of autonomous sensing, cognition, and the evolution of next-generation intelligent manufacturing systems.
The convergence of industrial knowledge graphs with emerging technologies like generative AI and edge computing represents a transformative opportunity for manufacturing organizations. Successful advanced industry (AI) companies are leveraging Industry 4.0 to achieve faster, more sustainable change, shown most dramatically at “lighthouse” manufacturers that have led the way in Industry 4.0 implementation. Through the Global Lighthouse Network (GLN), a research collaboration between the World Economic Forum and McKinsey on the future of production and the Fourth Industrial Revolution, 103 sites around the world have been identified as lighthouses, having successfully transformed their factories through Industry 4.0. These companies are leveraging digital technology to build more agile and customer-focused organizations. This approach lets manufacturers look beyond productivity in order to focus on improving their sustainability, agility, speed to market, customization, and customer satisfaction: a total of five areas of impact. [13]
As manufacturing complexity continues to increase, industrial knowledge graphs will become essential infrastructure for organizations seeking to capture the full value of their digital transformation investments. The companies that recognize this strategic opportunity and partner with proven solution providers will establish lasting competitive advantages in the intelligent manufacturing era.
To explore how industrial knowledge graphs can transform your manufacturing operations, visit SymphonyAI’s industrial platform and discover the comprehensive capabilities that leading manufacturers are using to achieve breakthrough operational performance.
