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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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“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
Kernel Methods and Machine Learning
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
Fast query execution for retrieval models based on path-constrained random walks
Lao, N., & Cohen, W. W. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835916 · Cited by 99
Rangan, R. M., Rohde, S. M., Peak, R. S., Chadha, B., & Bliznakov, P. I. (2005) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.2031270 · Cited by 92
Fontenla-Romero, Ó., Guijarro‐Berdiñas, B., Martínez‐Rego, D., Pérez‐Sánchez, B., & Peteiro-Barral, D. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-3942-3.ch002 · Cited by 83
A dynamic Bayesian network approach for digital twin
Li, C., Mahadevan, S., Ling, Y., Wang, L., & Choze, S. (2017) · AIAA Non-Deterministic Approaches Conference · doi:10.2514/6.2017-1566 · Cited by 48
Enterprise 2.0 and Semantic Technologies for Open Innovation Support
Carbone, F., Contreras, J., & Hernández, J. Z. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13025-0_3 · Cited by 15
The Design and Development of Manufacturing Process Knowledge Base System Based on Ontology
Haojie, S., Wang, H., Liu, T., Zhang, Q., & Gao, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46771-9_2 · Cited by 5
Rough-set-based process knowledge discovery approach in iron and steel industry
Ma, Y. (2009) · Cited by 2
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Digital twin paradigm: A systematic literature review
Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103469 · Cited by 1,129
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., Todd, M. D., Mahadevan, S., Hu, C. K., & Hu, Z. Y. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03425-4 · Cited by 511
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Wagg, D., Worden, K., Barthorpe, R. J., & Gardner, P. A. (2020) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering · doi:10.1115/1.4046739 · Cited by 382
Modular based flexible digital twin for factory design
Guo, J., Zhao, N., Sun, L., & Zhang, S. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0953-6 · Cited by 205
van der Valk, H., Haße, H., Möller, F., & Otto, B. (2021) · Business & Information Systems Engineering · doi:10.1007/s12599-021-00727-7 · Cited by 113
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Marai, O. E., Taleb, T., & Song, J. (2020) · IEEE Network · doi:10.1109/mnet.011.2000398 · Cited by 167
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Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review
Buchgeher, G., Gabauer, D., Martínez-Gil, J., & Ehrlinger, L. (2021) · IEEE Access · doi:10.1109/access.2021.3070395 · Cited by 158
A comprehensive survey on digital twin for future networks and emerging Internet of Things industry
Hakiri, A., Gokhale, A. S., Yahia, S. B., & Mellouli, N. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110350 · Cited by 181
Mo, F., Rehman, H. U., Monetti, F. M., Chaplin, J. C., SANDERSON, D. R., Popov, A. A., Maffei, A., & Ratchev, S. M. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102524 · Cited by 146
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Liu, Y. K., Ong, S. K., & Nee, A. Y. C. (2022) · Advances in Manufacturing · doi:10.1007/s40436-021-00375-w · Cited by 126
Trauer, J., Schweigert-Recksiek, S., Engel, C., Spreitzer, K., & Zimmermann, M. (2020) · Proceedings of the Design Society: DESIGN Conference · doi:10.1017/dsd.2020.15 · Cited by 112
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Ontologies and Artificial Intelligence Systems for the Cooperative Smart Farming Ecosystem
Chukkapalli, S. S. L., Mittal, S., Gupta, M., Abdelsalam, M., Joshi, A., Sandhu, R. S., & Joshi, K. P. (2020) · IEEE Access · doi:10.1109/access.2020.3022763 · Cited by 118
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Rosen, R., Boschert, S., & Sohr, A. (2018) · atp magazin · doi:10.17560/atp.v60i10.2371 · Cited by 179
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D’Amico, R. D., Erkoyuncu, J. A., Addepalli, S., & Penver, S. (2022) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2022.06.004 · Cited by 103
Digital twin for integration of design-manufacturing-maintenance: an overview
Fu, Y., Zhu, G., Zhu, M., & Xuan, F. (2022) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-022-00760-x · Cited by 98
From Simulation to Autonomy: Reviews of the Integration of Artificial Intelligence and Digital Twins
Sajadieh, S. M. M., & Noh, S. D. (2025) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-025-00750-z · Cited by 114
A Distributed Ledger Approach to Digital Twin Secure Data Sharing
Dietz, M., Putz, B., & Pernul, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22479-0_15 · Cited by 92
Next Generation Digital Twin: an Ecosystem for Mechatronic Systems?
Rosen, R., Fischer, J.-G., & Boschert, S. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.11.685 · Cited by 66
Rabah, S., Assila, A., Khouri, E., Maier, F., Ababsa, F., bourny, V., Maier, P., & Mérienne, F. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.10.070 · Cited by 86
Zheng, X., Psarommatis, F., Petrali, P., Turrin, C., Lu, J., & Kiritsis, D. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.10.044 · Cited by 84
Digital Twin Applications: A Survey of Recent Advances and Challenges
Mendonça, R. D. S., Lins, S. D. O., de Bessa, I. V., Ayres, F. A. C., de Medeiros, R. L. P., & de Lucena, V. F. (2022) · Processes · doi:10.3390/pr10040744 · Cited by 92
Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges
Tang, Q., Yu, F. R., Xie, R., Boukerche, A., Huang, T., & Liu, Y. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3175453 · Cited by 81
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Lu, J., Zheng, X., Gharaei, A., Kalaboukas, K., & Kiritsis, D. (2020) · Lecture notes in mechanical engineering · doi:10.1007/978-3-030-46212-3_7 · Cited by 73
Li, X., Wang, L., Chuanjun, Z., & Liu, Z. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.08.003 · Cited by 68
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Lu, J., Zhaorui, Y., Zheng, X., Wang, J., & Kiritsis, D. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09610-5 · Cited by 75
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Sahlab, N., Kamm, S., Müller, T., Jazdi, N., & Weyrich, M. (2021) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps49255.2021.9468219 · Cited by 64
Alimam, H., Mazzuto, G., Tozzi, N., Ciarapica, F. E., & Bevilacqua, M. (2023) · Journal of King Saud University: Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101846 · Cited by 60
The Modeling and Using Strategy for the Digital Twin in Process Planning
Zhao, P., Liu, J., Jing, X., Tang, M., Sheng, S., Zhou, H., & Liu, X. (2020) · IEEE Access · doi:10.1109/access.2020.2974241 · Cited by 64
Chhetri, S. R., Faezi, S., Canedo, A., & Faruque, M. A. A. (2019) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3302505.3310085 · Cited by 63
Su, C., Han, Y., Tang, X., Jiang, Q., Wang, T., & He, Q. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104101 · Cited by 65
Digital Twins-Based Smart Design and Control of Ultra-Precision Machining: A Review
Wu, L., Leng, J., & Ju, B. (2021) · Symmetry · doi:10.3390/sym13091717 · Cited by 59
Semantic-Enhanced Digital Twin System for Robot–Environment Interaction Monitoring
Li, X., He, B., Wang, Z., Zhou, Y., Li, G., & Jiang, R. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3066542 · Cited by 57
Zhang, X., Zheng, L., Fan, W. H., Ji, W., Mao, L., & Wang, L. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102609 · Cited by 58
Semantic models and knowledge graphs as manufacturing system reconfiguration enablers
Mo, F., Chaplin, J. C., SANDERSON, D. R., Martínez-Arellano, G., & Ratchev, S. M. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102625 · Cited by 54
Lin, C. J., Wang, K., Tadesse, A. A., & Woldegiorgis, B. H. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.12.001 · Cited by 53
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A Literature Review of the Digital Thread: Definition, Key Technologies, and Applications
Zhang, Q., Liu, J., & Chen, X. (2024) · Systems · doi:10.3390/systems12030070 · Cited by 51
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Wen, J., Gabryś, B., & Musiał, K. (2022) · IEEE Access · doi:10.1109/access.2022.3184801 · Cited by 50
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Liu, Y., Gu, F., Wu, Y., Gu, X., & Guo, J. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103753 · Cited by 26
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Chukkapalli, S. S. L., Pillai, N., Mittal, S., & Joshi, A. (2021) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi53945.2021.9624688 · Cited by 23
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Waszak, M., Lam, A. N., Hoffmann, V., Elvesæter, B., Mogos, M. F., & Roman, D. (2022) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c54293.2022.00014 · Cited by 21
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Rožanec, J. M., Lu, J., Košmerlj, A., Kenda, K., Kiritsis, D., Jovanoski, V., Rupnik, J., Karlovcec, M., & Fortuna, B. (2020) · Cited by 21
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Zheng, X., Petrali, P., Lu, J., Turrin, C., & Kiritsis, D. (2022) · Frontiers in Manufacturing Technology · doi:10.3389/fmtec.2022.901364 · Cited by 14
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Digital twin on concepts, enabling technologies, and applications
Bing, Z., Enyan, M., Amu‐Darko, J. N. O., Issaka, E., Liu, H., Junsen, R., & Zhang, X. (2024) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-024-04973-0 · Cited by 15
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Wu, S., Lu, J., Hu, Z., Yang, P., Wang, G., & Kiritsis, D. (2021) · International Conference on System of Systems Engineering · doi:10.1109/sose52739.2021.9497473 · Cited by 11
Sharma, A. K., Srivastava, M. K., & Sharma, R. (2025) · Global Business Review · doi:10.1177/09721509251314795 · Cited by 16
Mixed-model product disassembly sequence optimization based on cognitive digital twin
Qi, L., Xu, W., Wang, K., Liu, J., Ye, X., Yang, H., & Zhong, Y. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.07.004 · Cited by 10
Knowledge-enabled digital twin for smart designing of aircraft assembly line
Chang, X., Jia, X., Liu, K., & Hu, H. (2021) · Assembly Automation · doi:10.1108/aa-09-2020-0133 · Cited by 9
Semantic Data Mining in Ubiquitous Sensing: A Survey
Nalepa, G. J., Bobek, S., Kutt, K., & Atzmueller, M. (2021) · Sensors · doi:10.3390/s21134322 · Cited by 9
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Rajamurugu, N., & Karthik, M. K. (2022) · Wiley eBooks · doi:10.1002/9781119842316.ch2 · Cited by 10
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Automated Knowledge Graph Learning in Industrial Processes
Ammann, L., Martínez-Gil, J., Mayr, M., & Chasparis, G. C. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.01.303 · Cited by 8
Explainable knowledge graph embeddings for industrial process monitoring & control
Weyns, M., Blyau, T., Steenwinckel, B., De Turck, F., Van Hoecke, S., & Ongenae, F. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103242 · Cited by 7
TwinExplainer: Explaining Predictions of an Automotive Digital Twin
Neupane, S., Fernandez, I., Patterson, W., Mittal, S., Parmar, M., & Rahimi, S. (2023) · Conference on Algebraic Informatics · doi:10.1109/cai64502.2025.00186 · Cited by 13
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Yao, Z., Tan, L., & She, K. (2022) · Sensors · doi:10.3390/s22124607 · Cited by 7
A Comprehensive Survey on Digital Twin for Future Networks and Emerging Iot IndustryPreprint
Hakiri, A., Gokhale, A. S., Yahia, S. B., & Mellouli, N. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4535810 · Cited by 7
Li, H., Yan, X., Zhang, Y., Liu, G., Zhai, Z., Wang, X., Wang, H., Wen, X., Liu, Y., Zhang, G., et al. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acb0ee · Cited by 6
Structuring and Accessing Knowledge for Historical and Streaming Digital Twins.
Oakes, B., Meyers, B., Janssens, D., & Vangheluwe, H. L. (2021) · PolyPublie (École Polytechnique de Montréal) · Cited by 6
Digital Twin with a Perspective from Manufacturing Industry
Wang, H., Chen, S., Sami, M. S. U. I., Rahman, F., & Tehranipoor, M. M. (2020) · Springer eBooks · doi:10.1007/978-3-030-64448-2_2 · Cited by 6
Li, M., & Wang, H. (2022) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd54268.2022.9776063 · Cited by 5
Horvath, A.-S., & Pouliou, P. (2024) · CRC Press eBooks · doi:10.1201/9781003425724-46 · Cited by 7
Han, X., Liu, X., Wang, H., & Liu, G. (2024) · Journal of Engineering Design · doi:10.1080/09544828.2024.2332121 · Cited by 5
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Meckler, S., Steinmüller, H., & Harth, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-79463-7_20 · Cited by 5
Mo, F., Rehman, H. U., Ugarte, M., Carrera-Rivera, A., Minango, N. R., Monetti, F. M., Maffei, A., & Chaplin, J. C. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113484 · Cited by 4
Sidorenko, A., Meyers, B., Siatras, V., Burget, P., Kremser, P., Legler, T., Hermann, J., Groß, J., Xanthakis, E., Hamerník, J., et al. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-026-02828-0 · Cited by 4
Cognitive Digital Twin Generations: From Foundational Instruments to Meta-Cognitive Ecosystems
Kabashkin, I. (2026) · Information · doi:10.3390/info17030285 · Cited by 4
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Martínez-Gil, J., Hoch, T., Pichler, M., Heinzl, B., Moser, B., Kurniawan, K., Kiesling, E., & Krause, F. (2023) · Springer eBooks · doi:10.1007/978-3-031-46452-2_4 · Cited by 4
Linking an Asset and a Domain Specific Ontology for a Simple Asset TimeSeries Application
Kaed, C. E., Danilchenko, V., Delpech, F., Brodeur, J., & Radisson, A. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8621972 · Cited by 4
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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
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
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Wang, H., & Peng, G. (2023) · Springer eBooks · doi:10.1007/978-981-19-9626-9_6 · Cited by 3
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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
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Du, J., Zhong, H., Zhou, L., & Li, J. (2024) · ACM SIGGRAPH Posters · doi:10.1145/3641234.3671031 · Cited by 3
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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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