Installation
Since Ontolearn is a Python library, you will need to have Python installed on your system (currently supporting version 3.10.13 or higher). Since python comes in various versions and with different, sometimes conflicting dependencies, most guides will recommend to set up a “virtual environment” to work in and so do we.
To create a virtual python environments you can consider using the builtin python module venv or conda.
Installation via pip
Released versions of Ontolearn can be installed using pip, the
Package Installer for Python. pip comes as part of Python.
pip install ontolearn
This will download and install the latest release version of Ontolearn and all its dependencies from https://pypi.org/project/ontolearn/.
Installation From Source
To download the Ontolearn source code, you will also need to have a copy of the Git version control system conda installed.
Install java and curl:
# for Unix systems (Linux and macOS)
sudo apt install openjdk-11-jdk
sudo apt install curl
# for Windows please check online for yourself :)
Once you have the done previous step, you can continue setting up a virtual environment and installing the dependencies.
-> First download (clone) the source code
git clone https://github.com/dice-group/Ontolearn.git cd Ontolearn
-> Create and activate a conda virtual environment.
conda create -n venv python=3.10.14 --no-default-packages conda activate venv
-> Install the dependencies
pip install -e .
Now you are ready to develop on Ontolearn or use the library!
Verify installation
To test if the installation was successful, you can try this command: It will only try to load the main library file into Python:
python -c "import ontolearn"
Tests
You can run the tests as follows but make sure you have installed the external files using the commands described here to successfully pass all the tests:
pytest
Note: The tests are designed to run successfully on Linux machines since we also use them in GitHub Action. Therefore, trying to run them on a Windows machine can lead to some issues.
Download External Files
Some resources like pre-calculated embeddings or pre_trained_agents and datasets (ontologies)
are not included in the repository directly. Use the command wget to download them from our data server.
NOTE: Before you run the following commands in your terminal, make sure you are in the root directory of the project!
To download the datasets:
wget https://files.dice-research.org/projects/Ontolearn/KGs.zip -O ./KGs.zip
Then depending on your operating system, use the appropriate command to unzip the files:
# Windows
tar -xf KGs.zip
# or
# macOS and Linux
unzip KGs.zip
Finally, remove the .zip file:
rm KGs.zip
To download learning problems:
wget https://files.dice-research.org/projects/Ontolearn/LPs.zip
Follow the same steps to unzip as the in the KGs case.
Other Data
Below you will find the links to get the necesseray data for NCES, NCES2, ROCES and CLIP. The process to extract the data is the same as shown earlier with “KGs”.
#NCES:
https://files.dice-research.org/projects/NCES/NCES_Ontolearn_Data/NCESData.zip
#NCES2:
https://files.dice-research.org/projects/NCES/NCES_Ontolearn_Data/NCES2Data.zip
#ROCES:
https://files.dice-research.org/projects/NCES/NCES_Ontolearn_Data/ROCESData.zip
#CLIP:
https://files.dice-research.org/projects/Ontolearn/CLIP/CLIPData.zip
Building (sdist and bdist_wheel)
In order to create a distribution of the Ontolearn source code, typically when creating a new release,
it is necessary to use the build tool. It can be invoked with:
python -m build
# or
python setup.py bdist_wheel sdist
Distribution packages that are created, can then be published to the Python Package Index (PyPI) using twine.
py -m twine upload --repository pypi dist/*
Building the docs
The documentation can be built with
sphinx-build -M html docs/ docs/_build/
It is also possible to create a PDF manual, but that requires LaTeX to be installed:
sphinx-build -M latex docs/ docs/_build/
Simple Linting
You can lint check using flake8:
flake8
or ruff:
ruff check
Additionally, you can specify the path where you want to execute the linter.
In the next guide we give some example on the main usage of Ontolearn. The guides after that, goes into more details on the key concepts and functionalities used in Ontolearn.