Tutorial on
Semantic Foundations for Digital Twins: The Missing Knowledge Layer
Abstract
Digital Twins increasingly integrate heterogeneous data, models, observations, processes, and services to represent and interact with physical and industrial systems. However, connecting these heterogeneous resources does not necessarily mean that their semantics—their meaning, relationships, and context—are explicitly represented or consistently understood. This raises an important question: what is missing between connecting Digital Twin data and actually understanding what these data mean?
This tutorial introduces semantic foundations as a knowledge layer for Digital Twins. It explains how ontologies, semantic models, and Knowledge Graphs can make the meaning and relationships of heterogeneous Digital Twin resources explicit and machine-interpretable.
Starting from the limitations of purely data-centric integration, the tutorial will introduce the main semantic building blocks required to represent physical and digital entities, observations, properties, processes, relationships, and contextual information. It will then demonstrate how these representations can be connected within a Knowledge Graph and exploited for semantic interoperability, integrated querying, contextualisation, and reasoning.
A practical Digital Twin case study will serve as a common thread throughout the tutorial. Starting from heterogeneous data describing an industrial system, the case study will progressively introduce semantic modelling, ontology concepts and relationships, Knowledge Graph construction, semantic querying, and simple reasoning examples. The objective is to make the benefits of the semantic layer tangible rather than presenting semantic technologies only from a theoretical perspective.
The tutorial will conclude with a short discussion of how such semantic foundations can support the evolution towards more knowledge-aware and cognitive Digital Twins, opening perspectives for neuro-symbolic AI and knowledge-grounded AI agents.
Keywords
Digital Twins; Semantic Digital Twins; Semantic Interoperability; Ontologies; Knowledge Graphs; Knowledge Representation; Semantic Reasoning; Industry 4.0; Cognitive Digital Twins.
Aims and Learning Objectives
The tutorial aims to provide participants with a concise and practical understanding of why semantic foundations matter for Digital Twins and how a semantic knowledge layer can be constructed and exploited.
At the end of the tutorial, participants will be able to:
Identify the semantic challenges arising from heterogeneous data, models, and information sources in Digital Twin environments.
Understand the role of ontologies and Knowledge Graphs in providing explicit and machine-interpretable representations of Digital Twin entities, relationships, and context.
Explain the added value of a semantic knowledge layer for semantic interoperability, knowledge integration, contextualisation, querying, and reasoning.
Apply the main steps of semantic modelling to a simplified Digital Twin case study, from heterogeneous data to an integrated Knowledge Graph.
Recognise how semantic foundations can support future knowledge-aware and cognitive Digital Twins, including perspectives involving neuro-symbolic AI
Target Audience
The tutorial is intended for researchers, PhD students, engineers, and practitioners working with Digital Twins, Industry 4.0, cyber-physical systems, industrial interoperability, data integration, Knowledge Engineering, Semantic Web technologies, Knowledge Graphs, and Artificial Intelligence.
It is particularly relevant to participants dealing with heterogeneous Digital Twin data and models who want to understand how explicit semantics can improve their integration and exploitation.
The tutorial is designed to create a bridge between participants coming from industrial Digital Twin and systems engineering communities and those coming from Knowledge Engineering and Semantic Web communities.
Prerequisite Knowledge of Audience
No advanced knowledge of Semantic Web technologies is required.
Basic familiarity with Digital Twins, data modelling, information systems, or Knowledge Graphs may be useful but is not mandatory. The necessary concepts related to ontologies, Knowledge Graphs, semantic interoperability, and reasoning will be introduced during the tutorial.
The practical example will focus on the underlying concepts rather than requiring prior expertise in RDF, OWL, or SPARQL.
Detailed Outline
. The Missing Knowledge Layer: Why Do Digital Twins Need Semantics?
Building the Missing Layer: Semantic Foundations for Digital Twins
What Does the Knowledge Layer Enable?
Practical Case Study: From Heterogeneous Data to a Knowledge-Aware Digital Twin?
Beyond the Missing Layer: Perspectives