# Cognitive Twins for Supporting Decision-Makings of Internet of Things Systems

arXiv: https://arxiv.org/abs/1912.08547 · HTML: https://mlsanity.com/paper/1912.08547/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [Ontologies in Digital Twins: A Systematic Literature Review](https://mlsanity.com/paper/2308.15168/) (2023)
- [Actionable Cognitive Twins for Decision Making in Manufacturing](https://mlsanity.com/paper/2103.12854/) (2021)
- [Disruption Detection for a Cognitive Digital Supply Chain Twin Using Hybrid Deep Learning](https://mlsanity.com/paper/2309.14557/) (2023)

## Abstract (arXiv)

Cognitive Twins (CT) are proposed as Digital Twins (DT) with augmented semantic capabilities for identifying the dynamics of virtual model evolution, promoting the understanding of interrelationships between virtual models and enhancing the decision-making based on DT. The CT ensures that assets of Internet of Things (IoT) systems are well-managed and concerns beyond technical stakeholders are addressed during IoT system development. In this paper, a Knowledge Graph (KG) centric framework is proposed to develop CT. Based on the framework, a future tool-chain is proposed to develop the CT for the initiatives of H2020 project FACTLOG. Based on the comparison between DT and CT, we infer the CT is a more comprehensive approach to support IoT-based systems development than DT.