# Ontologies in Digital Twins: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2308.15168 · HTML: https://mlsanity.com/paper/2308.15168/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 162 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators](https://mlsanity.com/paper/1805.09979/) (2018)
- [Actionable Cognitive Twins for Decision Making in Manufacturing](https://mlsanity.com/paper/2103.12854/) (2021)
- [Cognitive Twins for Supporting Decision-Makings of Internet of Things Systems](https://mlsanity.com/paper/1912.08547/) (2019)
- [Populating the Digital Space for Cultural Heritage with Heritage Digital Twins](https://mlsanity.com/paper/2205.13206/) (2022)
- [The Heritage Digital Twin: a bicycle made for two. The integration of digital methodologies into cultural heritage research](https://mlsanity.com/paper/2302.07138/) (2023)

## Built on by (most cited first, 7 of 7)

- [Semantic Association Rule Learning from Time Series Data and Knowledge Graphs](https://mlsanity.com/paper/2310.07348/) (2023)
- [Learning Semantic Association Rules from Internet of Things Data](https://mlsanity.com/paper/2412.03417/) (2024)
- [AE SemRL: Learning Semantic Association Rules with Autoencoders](https://mlsanity.com/paper/2403.18133/) (2024)
- [Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias](https://mlsanity.com/paper/2505.01754/) (2025)
- [From USD Scenes to Knowledge Graphs: Zero-Shot Ontology Grounding with LLMs](https://mlsanity.com/paper/2606.09134/) (2026)
- [Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Tasks](https://mlsanity.com/paper/2608.05867/) (2026)
- [DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models](https://mlsanity.com/paper/2610.18635/) (2026)

## Abstract (arXiv)

Digital Twins (DT) facilitate monitoring and reasoning processes in cyber-physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs.