Overview

Python library for conveniently constructing and executing Machine Learning (ML) pipelines represented by Knowledge Graphs (KGs). It features a coding interface and a CLI, and allows the user to:

1. Construct an ML pipeline that gets a CSV as input and processes the data using any of the available tasks and methods.
2. Save the constructed pipeline as a KG in Turtle format.
3. Execute the generated KG.

The coding interface is demonstrated with three sample Python files. The pipelines represented by the generated sample KGs are briefly explained below:

1. ML pipeline: Loads features and labels from an input CSV dataset, splits the data, trains and tests a k-NN model, and visualizes the prediction errors.
2. Statistics pipeline: Loads a feature from an input CSV dataset, normalizes it, and plots its values (before and after normalization) using a scatter plot.
3. Visualization pipeline: Loads a feature from an input CSV dataset and plots its values using a line plot.

Under the hood, ExeKGLib uses well-known Python libraries for data processing and visualization and performing predictions such as pandas, matplotlib, and scikit-learn.

ExeKGLib is described in the following paper published as part of ESWC 2023:
Klironomos A., Zhou B., Tan Z., Zheng Z., Gad-Elrab M., Paulheim H., Kharlamov E. ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics