# ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics

arXiv: https://arxiv.org/abs/2305.02966 · HTML: https://mlsanity.com/paper/2305.02966/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 2 of 2)

- [pyWATTS: Python Workflow Automation Tool for Time Series](https://mlsanity.com/paper/2106.10157/) (2021)
- [The MATLAB Toolbox SciXMiner: User's Manual and Programmer's Guide](https://mlsanity.com/paper/1704.03298/) (2017)

## Built on by (most cited first, 3 of 3)

- [Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case](https://mlsanity.com/paper/2308.01105/) (2023)
- [ExeKGLib: A Platform for Machine Learning Analytics based on Knowledge Graphs](https://mlsanity.com/paper/2508.00394/) (2025)
- [Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning](https://mlsanity.com/paper/2603.19888/) (2026)

## Abstract (arXiv)

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.