ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics
This demo paper presents an incremental tool for ML practitioners to simplify pipeline construction, improving accessibility and workflow management.
The paper tackles the complexity of building machine learning pipelines by introducing ExeKGLib, a Python library that uses knowledge graphs to enable users with minimal ML knowledge to create transparent, reusable, and executable workflows, demonstrating its benefits compared to conventional code.
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.