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Generating Digital Twin Models using Knowledge Graphs for Industrial Production Lines
Open accessAbstract
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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“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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Cites
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Kung, S. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781139176224 · Cited by 289
Industrial IoT lifecycle via digital twins
Canedo, A. (2016) · International Conference on Hardware/Software Codesign and System Synthesis · doi:10.1145/2968456.2974007 · Cited by 167
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Birtic, M., Senington, R., & Syberfeldt, A. (2024) · Advances in transdisciplinary engineering · doi:10.3233/atde240171 · Cited by 1
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Tamboli, J., Joshi, K. P., & Clark, O. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402304 · Cited by 1
Self-citationCognitive Digital Twins: A Systematic Review of Definitions, Applications, and a Unified Definition
Bacnak, T. Z. B. T. Z., Arayıcı, Y., Doukari, O., Rogage, K., & Laing, R. (2026) · Information · doi:10.3390/info17060556 · Cited by 3
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Le, D. V., Zhou, S., Yang, J. Q., Chen, J., Ho, D., & Tan, R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13812-6.00003-8 · Cited by 1
González, S., García, Á., & Núñez, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.11216
Paulheim, H., Ristoski, P., & Portisch, J. (2023) · Synthesis lectures on data, semantics and knowledge · doi:10.1007/978-3-031-30387-6_8
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Seçkin, Z., Doğan, O., & İŞLER, F. (2024) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub504
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Benalla, A., Bekali, Y. K., & Saidi, R. (2025) · Springer eBooks · doi:10.1007/978-3-031-86837-5_18
Extracting Knowledge from Data in Lightweight Digital Twin Construction
Verde, L., de Biase, M. S., D’Angelo, G., Petruolo, R., Tirelli, P., Guarino, S., Sessa, A. D., Marzuillo, P., & Marrone, S. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.10.027
Exploring Digital Twin Models based on Knowledge Graphs
Tiwari, D. S. (2023) · Open Research Knowledge Graph (ORKG) · doi:10.48366/r659126
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Semantic modelling of AI-integrated digital twins for enhanced transparency
Shu, Y., Hellicar, A., WANG, W., & Rahman, A. (2026) · Journal of Intelligent Information Systems · doi:10.1007/s10844-026-01065-4
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Wang, J., Feng, L., Tang, X., & Yang, X. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-981-95-4717-3_4
Data Flow in Digital Twin-Driven Manufacturing System: From Modeling to Interaction
Liu, S., Zheng, P., & Bao, J. (2026) · Springer eBooks · doi:10.1007/978-981-95-8236-5_6
Benchmarking time-series foundation models for multitask predictive maintenance in digital twins
Ma, S., Flanigan, K. A., & Bergés, M. (2026) · Frontiers in Aerospace Engineering · doi:10.3389/fpace.2026.1937868
Li, H., Zhao, X., Wang, S., Yang, C., & Huang, W. (2026) · Frontiers in Computing and Intelligent Systems · doi:10.54097/ht37b711
Metadata
- 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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