chapter five

5 Modeling reality

 

This chapter covers

  • Contextualizing data and linking it semantically to create a model of reality
  • Ontologies as blueprints for the semantic understanding of models of reality
  • Modeling relationships in the real world with knowledge graphs
  • The importance of standardization
  • Serving the model of reality

We have looked at creating a digital representation of a physical system, using sensors to measure changes, and storing this data. But until these measurements are contextualized within an understanding of what they represent, how they relate, and what they indicate about system health, raw readings are merely numbers. This model must serve both the humans who interpret the system and the machines that act on it. A representation legible to only one limits the twin’s value.

The challenge is to structure knowledge about the physical system in a way that supports both human understanding and machine-readable automation. This means layering semantic meaning onto raw measurements by creating relationships among diverse data streams and establishing frameworks that enable the digital twin to contextualize, query, and apply rules to the information rather than merely store it.

5.1 Making sense of data

5.1.1 From measurements to decisions

5.1.2 The knowledge engineering challenge

5.2 Understanding context in digital twins

5.2.1 Types of context

5.2.2 How to contextualize data

5.2.3 The importance of context in generative AI

5.3 Ontologies as a blueprint for understanding

5.3.1 Core components of an ontology for a digital twin

5.3.2 Defining ontologies for digital twins

5.3.3 Choosing an ontology for your model

5.4 Knowledge graphs

5.4.1 Graph theory

5.4.2 Building a knowledge graph as a labeled property graph

5.4.3 Graph traversals

5.4.4 Building a knowledge graph of my home

5.4.5 Choosing a knowledge graph service

5.5 Standards and interoperability

5.5.1 A framework for implementing standards in your digital twin

5.5.2 Practical standards assessment