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Tutorial Setup

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.

Prerequisites

  • 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.

Audience

  • Python developers who need to work with KGs
  • Data Scientists, Data Engineers, Machine Learning Engineers
  • Technical Leaders who want hands-on KG implementation experience

Key Takeaways

  • 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

Installation

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 JupyterLab. To install using conda:

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 JupyterLab. 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:

jupyter-lab

Then open the examples subdirectory to launch the notebooks featured in the following sections of this tutorial.


Last update: 2021-01-06