Image by Cathrin2014 from Pixabay
In July 2023, Teresa Tung, managing director and cloud-first chief technologist at Accenture, gave a Factory of the Future talk at the Databricks Data + AI Summit on digital twins, knowledge graphs, and generative AI for warehouse automation. Two points she made that resonated with me: 1) Digital twins are for end-to-end automation, and 2) a knowledge graph in the mix can be “a twin of twins” that provides the semantic layer, the meaning and the means of interoperability between all the twins so that the system as a whole can deliver cohesive insight for overall optimization and other decision making. Stardog was the semantic graph database management system mentioned in Tung’s slide deck.
In the case of warehouse automation, as Tung pointed out, there are many interacting parts, including:
- Physical products being warehoused or transported
- Twins of different systems components in data product form, and then
- The “twin of twins” knowledge graph that enables these twins to interoperate.
All these elements need to be modeled individually before they’re brought together to interoperate and perform their roles in the system. That’s not to mention the role of generative AI (GAI) modeling, which makes it possible for factory managers to pose questions of the system via the graph in natural language, rather than using a database query language such as SQL or SPARQL. The GAI also enables its own take on predictive analytics.
Levels of interoperability
Wired co-founder Kevin Kelly’s Mirrorworld vision articulated in his 2019 article implies a world of interoperable digital twins, with each twin “mirroring” its physical world counterpart in terms of its behavior, including interactions with other twins. In the case of warehouse automation, for example, there’s the need for a twin that serves as a model of a plant to interoperate with a twin of an asset and a twin of an operational process.
At its most basic, one-to-one level, interoperability involves simple information sharing between components using the same software protocols, such as between a web page and a web browser. That level of sharing has existed for many decades and is the least scalable form of sharing.
Structural interoperability mandates the same data structure and syntax for the sharing to happen, allowing different kinds of software to share. Some limited scaling is possible.
Semantic interoperability requires a shared, organically growing resource of contextualized meaning–as in a well-designed knowledge graph–to enable enterprise- or even supply chain-wide interoperability scaling. Effective and scalable semantics is essential to a world of interoperable digital twins, and standards-based knowledge graphs make sense to serve as the semantic layer for broad, supply chain-scale sharing.
In this sense, the relationship and rule logic previously in applications becomes findable, accessible, interoperable, and reusable (FAIR) when it resides in the graph. What’s suggested here is the ability to use FAIR knowledge graphs (which are by definition standards-based) as a shared logic foundation for model-driven development. More on this later.
Because of the many sharable, combined contexts that enable scalable interoperation, FAIR knowledge graphs move us closer to a fuller form of machine-readable understanding and therefore more generalizable AI. Also implied here is the need to model an effective form of understanding that’s both machine and human comprehensible and manageable.
State-of-the-art digital twins in medical research
The modeling and simulation capabilities evident in digital twins are leading more organizations to fund digital twin initiatives as an aid to understanding the most complex interactions between systems. In 2022, The University of Nebraska-Lincoln awarded a $5 million grant to Nokia to build a digital twin of the human immune system over a five-year period.
The hope is that the twin Nokia builds will allow them to predict responses of the immune system to diseases and how disease behavior in the body changes over time. Eventually, the company hopes to build personalized immune system twins of patients.
Nokia obtained an additional infrastructure grant from the National Institutes of Health that will help them develop tooling for the immune system project. The company also partnered with the Johnny Carson Center for Emerging Media Arts for a medical artist to help design a user interface for the project.
Among other things, “The immune system keeps a record of every germ (microbe) it has ever defeated so it can recognize and destroy the microbe quickly if it enters the body again,” according to Australia’s Victoria State Government Department of Health on its Health Matters site. Nokia began its research by mathematically modeling T and helper T cells and how they connect. T cells are a type of white blood cells that fights infections.
Model-driven development for interoperating digital twins
Once relationship and rule logic such as Web Ontology Language (OWL), Shapes and Constraints Language (SHACL), and Datalog (a subset of the logic programming language Prolog) resides in a knowledge graph, that graph can serve as a driver of low-code development for a digital twin environment.
Semantic Arts estimates that 85 percent of code traditionally trapped in applications can be reused via knowledge graphs. One thing is certain: Lots of work is necessary to model all the twins and the twin of twins semantic layer, but the investment can pay off in a big way in terms of scalability and code writing reduction.
