5 Modeling reality

We have looked at how we can create a digital representation of a physical system, use sensors to measure how that system changes, and the ways in which we can integrate and store this data in a digital twin. But without some way to add meaning to this data, it remains a set of disconnected measurements that offer little insight into the true behavior of the physical system. Raw sensor readings such as temperature values, pressure measurements, or vibration frequencies are merely numbers until they are contextualized within an understanding of what they represent, how they relate to one another, and what patterns or anomalies might indicate about system health or performance.

The transformation from data to actionable intelligence requires layering understanding onto raw measurements. This involves creating relationships between diverse data streams and establishing the semantic frameworks necessary for a digital twin to interpret and reason about information, mirroring human expertise. This crucial step elevates the digital twin from a sophisticated monitoring tool to an intelligent system capable of reasoning and prediction.

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