Generating Digital Twin Models using Knowledge Graphs for Industrial Production Lines
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Digital Twin models are computerized clones of physical assets that can be used for in-depth analysis. Industrial production lines tend to have multiple sensors to generate near real-time status information for production. Industrial Internet of Things datasets are difficult to analyze and infer valuable insights such as points of failure, estimated overhead. etc. In this paper we introduce a simple way of formalizing knowledge as digital twin models coming from sensors in industrial production lines. We present a way on to extract and infer knowledge from large scale production line data, and enhance manufacturing process management with reasoning capabilities, by introducing a semantic query mechanism. Our system primarily utilizes a graph-based query language equivalent to conjunctive queries and has been enriched with inference rules.
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A knowledge graph-based digital twin model is composed of four parts (feature extraction, ontology creation, knowledge graph generation, and semantic relation extraction) and can extract and infer knowledge from large scale production line data and enhance manufacturing process management via semantic relation reasoning.
“In [227], a knowledge graph-based digital twin model is introduced which is composed of four parts, i.e., feature extraction, ontology creation, knowledge graph generation, and semantic relation extraction. It can extract and infer knowledge from large scale production line data and enhance manufacturing process management via semantic relation reasoning.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [227]
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Towards Building a Digital Twin of Complex System Using Causal Modelling
Jakovljevic, L., Kostadinov, D. D., Aghasaryan, A., & Palpanas, T. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-93409-5_40 · Cited by 4 · see citing works
Graph Modeling in Computer Assisted Automotive Development
Pakiman, A., & Garcke, J. (2022) · IEEE International Conference on Knowledge Graph (ICKG) · doi:10.1109/ickg55886.2022.00033 · Cited by 4 · see citing works
The Merging of Knowledge Management and New Information Technologies
Wang, H., & Peng, G. (2023) · Springer eBooks · doi:10.1007/978-981-19-9626-9_6 · Cited by 3 · see citing works
Cognitive Digital Twin Modeling of Robotic Disassembly Process
Qi, L., Yang, H., Liu, J., Xu, W., & Zhong, Y. (2024) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-52649-7_11 · Cited by 3 · see citing works
FactoryDecoder: Expertise-Free Digital Twin Generation and Modification Tool
Du, J., Zhong, H., Zhou, L., & Li, J. (2024) · ACM SIGGRAPH Posters · doi:10.1145/3641234.3671031 · Cited by 3 · see citing works
Supporting Digital Twin Integration Using Semantic Modeling and High-Level Architecture
Li, H., Lu, J., Zheng, X., Wang, G., & Kiritsis, D. (2021) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-85910-7_24 · Cited by 3 · see citing works
Simple Design Approach for Shared Digital Twins
Haße, H., & van der Valk, H. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.817 · Cited by 3 · see citing works
Challenges in Composite Digital Twin Models and their Impact on Interoperability
Memon, U., Mayer, W., Stumptner, M., & Selway, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-80775-6_28 · Cited by 3 · see citing works
Exploring the Concept of Cognitive Digital Twins from Model-Based Systems Engineering PerspectivePreprint
Lu, J., Yang, Z., Zheng, X., Wang, J., & Kiritsis, D. (2022) · Research Square · doi:10.21203/rs.3.rs-1431416/v1 · Cited by 4 · see citing works
A Flexible Digital Twin Framework for Poultry Feed Industrial Factory
Tarek, N., El-Hefnawy, N., Abdelkader, H. M., & Abdelatey, A. (2022) · International Conference on Computer Theory and Applications (ICCTA) · doi:10.1109/iccta58027.2022.10206352 · Cited by 2 · see citing works
- Authors
- Banerjee, A., Dalal, R., Mittal, S., & Joshi, K. P.
- Affiliations
- University of Maryland, Baltimore County (US)
- Venue
- Proceedings of the 2017 ACM on Web Science Conference
- Publisher
- ACM
- Published
- 2017-06-25
- Pages
- 425-430
- OpenAlex
- W2744820619
- Cited by
- 117
- References
- 9
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 21 · 2023: 12 · 2022: 22 · 2021: 20 · 2020: 12 · 2019: 4 · 2018: 4
- FWCI
- 8.03
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Locations
- ACM Web Science Conference (WebSci) (publishedVersion)
- Maryland Shared Open Access Repository (USMAI Consortium) (submittedVersion)
- Digital Repository at the University of Maryland (University of Maryland College Park)
- Funders
- International Business Machines Corporation
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Keywords
- digital twin, knowledge graphs, industrial production lines, Industrial Internet of Things, semantic queries, conjunctive queries, inference rules, knowledge extraction, production process management, sensor data analysis, reasoning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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