The coding exercises in the following tutorial are based on progressive examples based on cooking recipes, which illustrate the use of kglab and related libraries in Python for graph-based data science.
- Some coding experience in Python (you can read a 20-line program)
- Interest in use cases that require knowledge graph representation
Additionally, if you've completed Algebra 2 in secondary school and have some business experience working with data analytics – both can come in handy.
- Python developers who need to work with KGs
- Data Scientists, Data Engineers, Machine Learning Engineers
- Technical Leaders who want hands-on KG implementation experience
- Hands-on experience with popular open source libraries in Python for building KGs
- Coding examples that can be used as starting points for your own KG projects
- Understanding trade-offs for different approaches to building KGs
You can run the notebooks locally on a recent laptop. First clone the Git repository and install the dependencies:
git clone https://github.com/DerwenAI/kglab.git cd kglab pip install -r requirements.txt
Also make sure to install
To install using
conda install -c conda-forge jupyterlab
Or if you use
pip you can install it with:
pip install jupyterlab
For installing via
pip install --user you must add the user-level
bin directory to your
PATH environment variable in order to launch
If you're using a Unix derivative (FreeBSD, GNU/Linux, OS X), you can
achieve this by using the
export PATH="$HOME/.local/bin:$PATH" command.
Once installed, launch JupyterLab with:
Then open the
examples subdirectory to launch the notebooks featured
in the following sections of this tutorial.