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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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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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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