ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics
Antonis Klironomos, Baifan Zhou, Zhipeng Tan, Zhuoxun Zheng, Gad-Elrab, Mohamed, Heiko Paulheim, Evgeny Kharlamov

TL;DR
ExeKGLib is a Python library that leverages knowledge graphs to simplify the creation of transparent, reusable, and executable machine learning pipelines for users with minimal ML expertise.
Contribution
It introduces a novel approach using knowledge graphs to enhance the transparency and reusability of ML workflows in a user-friendly Python library.
Findings
Improves transparency of ML pipelines
Enhances reusability of ML workflows
Simplifies pipeline construction for users with minimal ML knowledge
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 its benefits.
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Taxonomy
TopicsAdvanced Graph Neural Networks · Explainable Artificial Intelligence (XAI) · Machine Learning in Materials Science
MethodsLib
