Why Knowledge Graphs Are Central to Industry 4.0 Initiatives
We are in the midst of a major technological inflection point: knowledge graphs have developed into a foundational component of the modern Industry 4.0 technology stack. I have this perspective because in my role at Cambridge Semantics I constantly engage with business and technology leaders and discuss data strategies, initiatives, and future-forward planning. I’m not alone in this viewpoint; the software industry sees this market opportunity too, and most major technology vendors have positioned offerings.
Knowledge graphs are not a “new” technology. For years, proponents of knowledge graphs have touted their data integration capabilities. Today, knowledge graphs are increasing automation and reducing decision cycles, drastically optimizing data usage and efficiently driving down costs. This is because knowledge graphs semantically harmonize access to diverse data sources, resulting in machine readable and human understandable data. Now, the suite of demands and challenges in Industry 4.0 environments have pushed beyond traditional data landscape capabilities and propelled market interest toward knowledge graphs.
Before diving into the current explosion of interest, let me quickly establish the key requirements for the knowledge graph stack.
Set those aside for a minute, and let’s discuss some key factors driving the growth of knowledge graph technologies.
Digital twins are inherently intricate, combining data from several sources as they reflect an entity’s state across its lifecycle. The state of the entity and its relationships cannot always be easily modeled using a traditional schema. For example, a bill of materials for a given design has a hierarchical relationship between entities and sub-assemblies within a particular entity which present a challenge for a relational database. A knowledge graph is a natural way to express these relationships and create validation rules to ensure compliance.
Another complex use case is the enterprise supply chain which can be modeled in a network (graph) form. Supply chains are akin to Complex Adaptive Systems which feature ongoing emergent and divergent relationships. The data architecture supporting the robust supply chain must be adaptable and gracefully cope with change as a constant. Knowledge Graphs — especially those built on W3C OWL/RDF — are ideal for such systems.
Knowledge graphs have long been understood to be particularly useful in analyzing specific types of systems. Graph algorithms provide a natural means of calculating shortest paths, system throughput, community detection, node importance, and node similarity (graph embeddings).
What’s become increasingly clear is that these techniques can also augment traditional machine learning. Researchers and academics have realized that robotic vision algorithms can benefit from logic available solely through semantic reasoning. One early adopter, Amazon, is betting that this technology can eventually lead to a fleet of autonomous robots. Having a semantic understanding of the fulfillment center is optimizing the movement of robots as they navigate the busy floor and in the future may help with robotic grasping and other tasks.
Artificial Intelligence is certainly having a moment. ChatGPT has captured the public’s attention, garnering headline articles in the New York Times and other newspapers. Personally, I’m more interested in the capabilities of the GPT-3 model (third-generation Generative Pre-trained Transformer). Over the past few months, I’ve experimented with GPT-3’s capabilities via their public API and I was stunned.
