Explainable knowledge graph embeddings for industrial process monitoring & control

Advances in Neural Information Processing Systems 19

(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,037 · see citing works

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

(2016) · doi:10.1145/2939672 · Cited by 6,563 · see citing works

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

Yang, B., Yih, W.-T., He, X., Gao, J., & Deng, L. (2014) · arXiv · doi:10.48550/arxiv.1412.6575 · Cited by 2,252 · see citing works

A Review of Relational Machine Learning for Knowledge Graphs

Nickel, M., Murphy, K. P., Tresp, V., & Gabrilovich, E. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2483592 · Cited by 1,684 · see citing works

Depth-first iterative-deepening

Korf, R. E. (1985) · Artificial Intelligence · doi:10.1016/0004-3702(85)90084-0 · Cited by 1,621 · see citing works

Modeling Relation Paths for Representation Learning of Knowledge Bases

Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., & Liu, S. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1082 · Cited by 657 · see citing works

Internet of things for smart factories in industry 4.0, a review

Soori, M., Behrooz Arezoo, B., & Dastres, R. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.006 · Cited by 636 · see citing works

European Semantic Web Conference 2009

Nielsen, F. Å. (2009) · Cited by 553 · see citing works

OWL-S: Semantic Markup for Web Services

Martin, D., Burstein, M., Hobbs, J., et al. (2004) · ePrints Soton (University of Southampton) · Cited by 492 · see citing works

SimplE Embedding for Link Prediction in Knowledge Graphs

Kazemi, S. M., & Poole, D. (2018) · arXiv · doi:10.48550/arxiv.1802.04868 · Cited by 476 · see citing works

Machine learning and deep learning based predictive quality in manufacturing: a systematic review

Tercan, H., & Meisen, T. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-01963-8 · Cited by 390 · see citing works

Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats

Dobbelaere, M. R., Plehiers, P. P., de Vijver, R. V., Stevens, C. V., & Geem, K. M. V. (2021) · Engineering · doi:10.1016/j.eng.2021.03.019 · Cited by 339 · see citing works

Recent trends on hybrid modeling for Industry 4.0

Sansana, J., Joswiak, M. N., Castillo, I., Wang, Z., Rendall, R., Chiang, L. H., & Reis, M. S. (2021) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2021.107365 · Cited by 307 · see citing works

Knowledge graph completion via complex tensor factorization

(2017) · arXiv (Cornell University) · doi:10.5555/3122009.3208011 · Cited by 211 · see citing works

Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models

Bikmukhametov, T., & Jäschke, J. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106834 · Cited by 198 · see citing works

A hybrid science‐guided machine learning approach for modeling chemical processes: A review

Sharma, N., & Liu, Y. A. (2022) · AIChE Journal · doi:10.1002/aic.17609 · Cited by 196 · see citing works

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Berrouyne, I., Adda, M., Mottu, J., et al. (2019) · doi:10.1145/3297280 · Cited by 193 · see citing works

Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives

Hu, Y., & Man, Y. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113405 · Cited by 177 · see citing works

TBox and ABox Reasoning in Expressive Description Logics.

Giacomo, G. D., & Lenzerini, M. (1996) · Cited by 164 · see citing works

PCA-SDG based process monitoring and fault diagnosis

Vedam, H., & Venkatasubramanian, V. (1999) · Control Engineering Practice · doi:10.1016/s0967-0661(99)00040-4 · Cited by 164 · see citing works

OntoCAPE—A large-scale ontology for chemical process engineering

Morbach, J., Yang, A., & Marquardt, W. (2006) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2006.06.010 · Cited by 159 · see citing works

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 · see citing works

Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing

Hsu, C.-Y., & Liu, W. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01591-0 · Cited by 156 · see citing works

Machine learning in process systems engineering: Challenges and opportunities

Daoutidis, P., Lee, J. H., Rangarajan, S., Chiang, L. H., Gopaluni, R. B., Schweidtmann, A. M., Harjunkoski, I., Mercangöz, M., Mesbah, A., Boukouvala, F., et al. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108523 · Cited by 147 · see citing works

Industrial data science – a review of machine learning applications for chemical and process industries

Mowbray, M., Vallerio, M., Pérez-Galván, C., Zhang, D., del Rio‐Chanona, E. A., & Navarro-Brull, F. J. (2022) · Reaction Chemistry & Engineering · doi:10.1039/d1re00541c · Cited by 140 · see citing works

Semantic Web and Knowledge Graphs for Industry 4.0

Yahya, M., Breslin, J. G., & Ali, M. I. (2021) · Applied Sciences · doi:10.3390/app11115110 · Cited by 135 · see citing works

Infusing theory into deep learning for interpretable reactivity prediction

Wang, S., Pillai, H. S., Wang, S., Achenie, L. E. K., & Xin, H. (2021) · Nature Communications · doi:10.1038/s41467-021-25639-8 · Cited by 133 · see citing works

The online use of first-principles models in process operations: Review, current status and future needs

Pantelides, C. C., & Renfro, J. G. (2012) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2012.07.008 · Cited by 129 · see citing works

Making knowledge graphs work for smart manufacturing: Research topics, applications and prospects

Wan, Y., Liu, Y., Chen, Z., Chen, C., Li, X., Hu, F., & Packianather, M. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.07.009 · Cited by 125 · see citing works

Generating Digital Twin Models using Knowledge Graphs for Industrial Production Lines

Banerjee, A., Dalal, R., Mittal, S., & Joshi, K. P. (2017) · Proceedings of the 2017 ACM on Web Science Conference · doi:10.1145/3091478.3162383 · Cited by 117 · see citing works

FLAGS: A methodology for adaptive anomaly detection and root cause analysis on sensor data streams by fusing expert knowledge with machine learning

Steenwinckel, B., De Paepe, D., Vanden Hautte, S., Heyvaert, P., Bentefrit, M., Moens, P., Dimou, A., Van Den Bossche, B., De Turck, F., Van Hoecke, S., et al. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.015 · Cited by 102 · see citing works

Composing Relationships with Translations

García-Durán, A., Bordes, A., & Usunier, N. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1034 · Cited by 97 · see citing works

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Aguiar-Pérez, J. M., & Pérez-Juárez, M. A. (2023) · Sensors · doi:10.3390/s23031467 · Cited by 95 · see citing works

TransGCN

Cai, L., Yan, B., Mai, G., Janowicz, K., & Zhu, R. (2019) · International Conference on Knowledge Capture · doi:10.1145/3360901.3364441 · Cited by 88 · see citing works

Advances in computers, Vol. 2

(1962) · Journal of the Franklin Institute · doi:10.1016/0016-0032(62)91111-0 · Cited by 88 · see citing works

Integration of machine learning and first principles models

Rajulapati, L. K., Chinta, S., Shyamala, B., & Rengaswamy, R. (2022) · AIChE Journal · doi:10.1002/aic.17715 · Cited by 77 · see citing works

The knowledge graph as the default data model for learning on heterogeneous knowledge

Wilcke, X., Bloem, P., & de Boer, V. (2017) · Data Science · doi:10.3233/ds-170007 · Cited by 68 · see citing works

Process structure-based recurrent neural network modeling for predictive control: A comparative study

Alhajeri, M. S., Luo, J., Wu, Z., Albalawi, F., & Christofides, P. (2022) · Chemical engineering research & design · doi:10.1016/j.cherd.2021.12.046 · Cited by 62 · see citing works

A generic hybrid model development for process analysis of industrial fixed-bed catalytic reactors

Azarpour, A., Borhani, T. N. G., Alwi, S. R. W., et al. (2016) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2016.10.024 · Cited by 59 · see citing works

Construction and Evolution of Fault Diagnosis Knowledge Graph in Industrial Process

Han, H., Wang, J., Wang, X., & Chen, S. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3200429 · Cited by 57 · see citing works

Efficient data integration in the railway domain through an ontology-based methodology

Verstichel, S., Ongenae, F., Loeve, L., et al. (2010) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2010.10.003 · Cited by 56 · see citing works

Systematic knowledge modeling and extraction methods for manufacturing process planning based on knowledge graph

Wen, P., Ma, Y., & Wang, R. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102172 · Cited by 54 · see citing works

Intelligent vision-based online inspection system of screw-fastening operations in light-gauge steel frame manufacturing

Martı́nez, P., Al‐Hussein, M., & Ahmad, R. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-05695-y · Cited by 51 · see citing works

Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant

Jablonka, K. M., Charalambous, C., Fernández, E. S., Wiechers, G., Monteiro, J. G. M., Moser, P., Smit, B., & García, S. (2023) · Science Advances · doi:10.1126/sciadv.adc9576 · Cited by 51 · see citing works

Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement

Rodriguez-Granrose, D., Jones, A., Loftus, H., Tandeski, T., Heaton, W., Foley, K. T., & Silverman, L. I. (2021) · Bioprocess and Biosystems Engineering · doi:10.1007/s00449-021-02529-3 · Cited by 47 · see citing works

Acid/epoxy reaction catalyst screening for low temperature (120°C) powder coatings

Merfeld, G. D., Molaison, C., Koeniger, R., et al. (2004) · Progress in Organic Coatings · doi:10.1016/j.porgcoat.2004.09.004 · Cited by 37 · see citing works

INK: knowledge graph embeddings for node classification

Steenwinckel, B., Vandewiele, G., Weyns, M., Agozzino, T., Turck, F. D., & Ongenae, F. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-021-00806-z · Cited by 35 · see citing works

FedCKE: Cross-Domain Knowledge Graph Embedding in Federated Learning

Huang, W. X., Liu, J., Li, T., Ji, S., Wang, D., & Huang, T. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3205705 · Cited by 34 · see citing works

Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes

Stavropoulou, G., Tsitseklis, K., Mavraidi, L., Chang, K. E., Zafeiropoulos, A., Karyotis, V. A., & Papavassiliou, S. (2024) · Sensors · doi:10.3390/s24082618 · Cited by 34 · see citing works

Proceedings of the 10th International Conference on Knowledge Capture

(2019) · doi:10.1145/3360901 · Cited by 28 · see citing works

XAI-KG: Knowledge Graph to Support XAI and Decision-Making in Manufacturing

Rožanec, J. M., Zajec, P., Kenda, K., Novalija, I., Fortuna, B., & Mladenić, D. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-79022-6_14 · Cited by 26 · see citing works

Proceedings of the 6th workshop on Workflows in support of large-scale science

Montagnat, J., & Taylor, I. (2011) · doi:10.1145/2110497 · Cited by 17 · see citing works

Enriching Artificial Intelligence Explanations with Knowledge Fragments

Rožanec, J. M., Trajkova, E., Novalija, I., Zajec, P., Kenda, K., Fortuna, B., & Mladenić, D. (2022) · Future Internet · doi:10.3390/fi14050134 · Cited by 16 · see citing works

Toward sustainable process industry based on knowledge graph: a case study of papermaking process fault diagnosis

Liang, X., Zhang, Q., Man, Y., et al. (2024) · Discover Sustainability · doi:10.1007/s43621-024-00259-6 · Cited by 15 · see citing works

LLMs for XAI: Future Directions for Explaining ExplanationsPreprint

Zytek, A., Pidò, S., & Veeramachaneni, K. K. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.06064 · Cited by 11 · see citing works

KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks

Heist, N., Hertling, S., & Paulheim, H. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615241 · Cited by 10 · see citing works

Stability assessment of liquid formulations: A deep learning approach

Micco, M. D., Gragnaniello, D., Zonfrilli, F., et al. (2022) · Chemical Engineering Science · doi:10.1016/j.ces.2022.117991 · Cited by 6 · see citing works

TALK: Tracking Activities by Linking Knowledge

Steenwinckel, B., Brouwer, M. D., Stojchevska, M., Turck, F. D., Hoecke, S. V., & Ongenae, F. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106076 · Cited by 6 · see citing works

TraceWalk: Semantic-based Process Graph Embedding for Consistency CheckingPreprint

Chen, Q., Wen, L., & Kumar, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.06883 · Cited by 2 · see citing works

PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for ManufacturingPreprint

Nordsieck, R., Schweizer, A., Heider, M., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.08371 · Cited by 1 · see citing works

Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs

Moens, P., Steenwinckel, B., Ongenae, F., Volckaert, B., & Hoecke, S. V. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3652304 · Cited by 6 · see citing works

RDF2vec at Scale

Paulheim, H., Ristoski, P., & Portisch, J. (2023) · Synthesis lectures on data, semantics and knowledge · doi:10.1007/978-3-031-30387-6_5

Low-Dimensional Federated Knowledge Graph Embedding via Knowledge DistillationPreprint

Zhang, X., Zeng, Z., Zhou, X., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.05748

Environmental control and the perception of control

Revesman, M. E., & Perlmuter, L. C. (1981) · Motivation and Emotion · doi:10.1007/bf00992550 · Cited by 5 · see citing works

FedE: Embedding Knowledge Graphs in Federated SettingPreprint

Chen, M., Zhang, W., Yuan, Z., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12882 · Cited by 8 · see citing works

Data Analysis Based on Knowledge Graph

Xie, K., Jia, Q., Jing, M., et al. (2020) · Lecture notes in networks and systems · doi:10.1007/978-3-030-61108-8_37 · Cited by 7 · see citing works