Knowledge Graphs as Enhancers of Intelligent Digital Twins

Sahlab, Nada; Kamm, Simon; Müller, Timo; Jazdi, Nasser; Weyrich, Michael

doi:10.1109/icps49255.2021.9468219

Cited by 41 publications

(14 citation statements)

References 23 publications

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“…The framework details the core parts of DTs and describes which parts need to be provided, interconnected and integrated to achieve different DT utilizations. Sahlab et al [ 97 ] propose using knowledge graphs to relate complex and dynamic digital models. They argue that the inherent extensibility, adaptability and semantic expressiveness make knowledge graphs suitable to manage models and enable reasoning to gain new insights.…”

Section: Digital Twin Modelingmentioning

confidence: 99%

Design, Modeling and Implementation of Digital Twins

Segovia

García-Alfaro

2022

Sensors

157

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A Digital Twin (DT) is a set of computer-generated models that map a physical object into a virtual space. Both physical and virtual elements exchange information to monitor, simulate, predict, diagnose and control the state and behavior of the physical object within the virtual space. DTs supply a system with information and operating status, providing capabilities to create new business models. In this paper, we focus on the construction of DTs. More specifically, we focus on determining (methodologically) how to design, create and connect physical objects with their virtual counterpart. We explore the problem into several phases: from functional requirement selection and architecture planning to integration and verification of the final (digital) models. We address as well how physical components exchange real-time information with DTs, as well as experimental platforms to build DTs (including protocols and standards). We conclude with a discussion and open challenges.

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Section: Digital Twin Modelingmentioning

confidence: 99%

Design, Modeling and Implementation of Digital Twins

Segovia

García-Alfaro

2022

Sensors

157

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“…Only then interaction between intelligent DTs can take place and they are able to proactively negotiate with others so that, for example, optimal process flows emerge. Sahlab et al [ 11 ] use knowledge graphs to refine intelligent DTs. Particularly in industrial applications, these approaches are distinguished by the management of dynamically emerging DTs.…”

Section: Related Workmentioning

confidence: 99%

“…With the development of semantic modeling, hardware, and communication technology, there are more degrees of freedom to leverage the semantic representation of DTs improving their usability [ 10 ]. For the internal interconnection in particular, the referencing of knowledge correlations distinguishes intelligent DTs [ 11 ]. The analysis of relevant literature reveals a research gap in the combination of both approaches (RDM and DTs), which the authors intend to address.…”

Section: Introductionmentioning

confidence: 99%

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

Lehmann

Schorz

Rache

et al. 2023

Sensors

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One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort.

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“…DTs following this architecture adopt ad hoc solutions for data management issues such as data extraction and integration of heterogeneous sources, data sanity, data transformation and enrichment, and data consumption by the virtual environment. The existence of data silos, the volume of data, and issues related to handling multiple, heterogeneous data sources, formats, and data types are often mentioned as significant challenges (SUN et al, 2020;VIVI et al, 2019;SINGH et al, 2021;SAHLAB et al, 2021).…”

Section: List Of Figuresmentioning

confidence: 99%

“…(OAKES et al, 2021) recommends using knowledge graphs for semantic queries through the GraphQL query language and APIs that allow queries in JSON format. (SAHLAB et al, 2021) makes a case for knowledge graphs and semantic queries, without referring to any specific query language.…”

Section: Data Search and Discoverymentioning

confidence: 99%

Data management in digital twins: a systematic literature review

2023

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The Internet of Things (IoT), personal and wearable devices, and continuous advances in data-gathering techniques have significantly increased the amount of relevant data that can be leveraged for innovative real-time, data-driven applications. Digital Twins (DTs) are virtual representations of physical objects which are fully integrated and in which the automatic data exchange occurs in a bidirectional way. DTs and big data are mutually reinforcing technologies since huge volumes of data representing the physical/virtual worlds are collected, transformed, and generated through models to aggregate value to the business. Modern DTs follow a five-component architecture, which includes a Data Management (DM) component that bridges a physical system, a mirrored virtual one, and services components. However, there is no clarity on the functionality required for the DM component. This work presents a Systematic Literature Review on DM issues and proposed solutions in the DT context. We analyzed DM under the big data value chain activities, highlighting key issues to be addressed: data heterogeneity, interoperability, integration, data search/discovery, and quality. In addition to surveying existing solutions for handling these issues, we contextualized them in the domain and function for which the DT was proposed, the type of data dealt with, and the technical infrastructure. The compilation of these solutions sheds light on the functionality of the DM component in a DT, trends, and opportunities. Our main findings revealed that the maturity level assumed for the DM component is at an early stage. The most mature solutions were proposed for the industry domain, and many of them assume humans as the ultimate information consumers. Data integration is the prevalent DM issue addressed due to the bridging role of the DM component, and cloud computing is the key implementation technology. Among the research opportunities are reference data management architectures, adoption of industry standards and ontologies, interoperability among distinct DTs, the development of agnostic standard implementations, and data provenance mechanisms.

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Knowledge Graphs as Enhancers of Intelligent Digital Twins

Cited by 41 publications

References 23 publications

Design, Modeling and Implementation of Digital Twins

Design, Modeling and Implementation of Digital Twins

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

Data management in digital twins: a systematic literature review

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