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ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics

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The Semantic Web: ESWC 2023 Satellite Events (ESWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13998))

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Abstract

Many machine learning (ML) libraries are accessible online for ML practitioners. Typical ML pipelines are complex and consist of a series of steps, each of them invoking several ML libraries. In this demo paper, we present ExeKGLib, a Python library that allows users with coding skills and minimal ML knowledge to build ML pipelines. ExeKGLib relies on knowledge graphs to improve the transparency and reusability of the built ML workflows, and to ensure that they are executable. We demonstrate the usage of ExeKGLib and compare it with conventional ML code to show ExeKGLib ’s benefits.

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Notes

  1. 1.

    https://aws.amazon.com/sagemaker

  2. 2.

    https://cloud.google.com/automl

  3. 3.

    https://github.com/boschresearch/ExeKGLib#usage

  4. 4.

    https://github.com/boschresearch/ExeKGLib/tree/main/examples

  5. 5.

    https://github.com/boschresearch/ExeKGLib#executing-an-ml-pipeline

  6. 6.

    https://bit.ly/exe-kg-lib-visualizations

  7. 7.

    https://github.com/boschresearch/ExeKGLib#kg-schemata

References

  1. Abreu, P.H., Santos, M.S., Abreu, M.H., Andrade, B., Silva, D.C.: Predicting breast cancer recurrence using machine learning techniques: a systematic review. ACM Comput. Surv. 49(3), 52:1–52:40 (2016). https://doi.org/10.1145/2988544

  2. Bartschat, A., Reischl, M., Mikut, R.: Data mining tools. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 9(4), e1309 (2019). https://doi.org/10.1002/widm.1309

    Article  Google Scholar 

  3. Heidrich, B., et al.: pyWATTS: python workflow automation tool for time series. arXiv preprint arXiv:2106.10157 (2021). https://doi.org/10.48550/arXiv.2106.10157

  4. Huang, Z., Fey, M., Liu, C., Beysel, E., Xu, X., Brecher, C.: Hybrid learning-based digital twin for manufacturing process: modeling framework and implementation. Robot. Comput.-Integr. Manuf. 82, 102545 (2023). https://doi.org/10.1016/j.rcim.2023.102545

    Article  Google Scholar 

  5. Kim, J., Ahn, I.: Infectious disease outbreak prediction using media articles with machine learning models. Sci. Rep. 11(1), 4413 (2021). https://doi.org/10.1038/s41598-021-83926-2

    Article  Google Scholar 

  6. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotech. J. 13, 8–17 (2015). https://doi.org/10.1016/j.csbj.2014.11.005

    Article  Google Scholar 

  7. Libbrecht, M.W., Noble, W.S.: Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16(6), 321–332 (2015). https://doi.org/10.1038/nrg3920

    Article  Google Scholar 

  8. Meng, L., et al.: Machine learning in additive manufacturing: a review. JOM 72(6), 2363–2377 (2020). https://doi.org/10.1007/s11837-020-04155-y

    Article  Google Scholar 

  9. Mikut, R., et al.: The MATLAB toolbox SciXMiner: user’s manual and programmer’s guide. arXiv preprint arXiv:1704.03298 (2017). https://doi.org/10.48550/arXiv.1704.03298

  10. Obulesu, O., Mahendra, M., ThrilokReddy, M.: Machine learning techniques and tools: a survey. In: 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 605–611. IEEE (2018). https://doi.org/10.1109/ICIRCA.2018.8597302

  11. Sarker, I.H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2(3), 1–21 (2021). https://doi.org/10.1007/s42979-021-00592-x

    Article  MathSciNet  Google Scholar 

  12. Zeng, L., Al-Rifai, M., Chelaru, S., Nolting, M., Nejdl, W.: On the importance of contextual information for building reliable automated driver identification systems. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–8. IEEE (2020). https://doi.org/10.1109/ITSC45102.2020.9294439

  13. Zheng, Z., et al.: Executable knowledge graphs for machine learning: a Bosch case of welding monitoring. In: Sattler, U., et al. The Semantic Web - ISWC 2022. ISWC 2022, LNCS, vol. 13489, pp. 791–809. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19433-7_45

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Acknowledgements

The work was partially supported by EU projects Dome 4.0 (GA 953163), OntoCommons (GA 958371), DataCloud (GA 101016835), Graph Massiviser (GA 101093202), and EnRichMyData (GA 101093202).

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Correspondence to Antonis Klironomos .

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Klironomos, A. et al. (2023). ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics. In: Pesquita, C., et al. The Semantic Web: ESWC 2023 Satellite Events. ESWC 2023. Lecture Notes in Computer Science, vol 13998. Springer, Cham. https://doi.org/10.1007/978-3-031-43458-7_23

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  • DOI: https://doi.org/10.1007/978-3-031-43458-7_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43457-0

  • Online ISBN: 978-3-031-43458-7

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