5 Modeling reality

We have looked at creating a digital representation of a physical system, using sensors to measure change, and storing this data. But without meaning, it remains disconnected measurements. Raw readings like temperature, pressure, and vibration are merely numbers until contextualized within an understanding of what they represent, how they relate, and what they indicate about system health. Critically, this model must serve both humans who interpret the system and 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 a digital twin to interpret and reason about 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 standards implementation in your digital twin

5.5.2 Practical standards assessment

5.5.3 A practical assessment framework

5.5.4 Real-world example: the asset administration shell (AAS)