Welcome to the KGTK notebook tutorial

The goal of this tutorial repository is to introduce the functionality of KGTK to first-time users. The Knowledge Graph Toolkit (KGTK) is a comprehensive framework for the creation and exploitation of large hyper-relational knowledge graphs (KGs), designed for ease of use, scalability, and speed. The tutorial consists of several notebooks that demonstrate how to perform network analysis, graph profiling, knowledge enrichment, and embedding computation over a portion of the Wikidata knowledge graph. The tutorial notebooks can be found in the tutorial folder. All notebooks require minimum configuration and can be run locally or in Google Colab in a matter of a few minutes. The input data for the notebooks is stored in the datasets folder. Basic understanding of knowledge graphs is sufficient for this tutorial.

This repository has been created for the purpose of the KGTK tutorial presented at ISWC 2021. For more information on this tutorial, see our website.

Notebooks

1. 01-kgtk-introduction.ipynb introduction to kgtk and kypher.
2. 02-kg-profiling.ipynb performs profiling of a Wikidata subgraph, by computing deep statistics of its classes, instances, and properties.
3. 03-kg-graph-embeddings.ipynb computes graph embeddings of a Wikidata subgraph using kgtk, demonstrates how to use these embeddings for similarity estimation, and visualizes them.
4. 04-kg-enrichment-with-csv.ipynb shows how structured data from IMDb can be integrated into a subset of Wikidata.
5. 05-kg-enrichment-with-lod.ipynb shows how LOD graphs like Getty Vocabulary can be used to enrich Wikidata by using kgtk operations.
6. 06-kg-network-analysis.ipynb analyzes the family network of Arnold Schwarzenegger (Q2685) in Wikidata by using KGTK operations.
7. 07-kg-constraint-validation.ipynb demonstrates how to do constraint validation on one wikidata property.

Running the notebooks in Google Colab

List of steps required to be able to run the ISI Google colab Notebooks.

Make a copy of the notebooks to your Google Drive.

The following tutorial notebooks are available to run in Google Colab

1. 01-kgtk-introduction.ipynb
2. 02-kg-profiling.ipynb
3. 03-kg-graph-embeddings.ipynb
4. 04-kg-enrichment-with-csv.ipynb
5. 05-kg-enrichment-with-lod.ipynb
6. 06-kg-network-analysis.ipynb
7. 07-kg-constraint-validation.ipynb

Click on a link, it'll take you to the Google Colab notebook. These are readonly notebook links.

Click on Save a copy in Drive from the File menu as shown.

This will create a copy of the notebook in your Google Drive.

Install kgtk

Run the first cell to install kgtk.

If you see this warning,

click on Run anyway to continue

You'll see an error after the install finishes,

This is because of a conflict in Google Colab's python environment. You have to click on the Restart Runtime button.

You do not have to install kgtk again.

In some notebooks, there are a few more installation cells, in case you see the same error as above, please click on Restart Runtime

Run the cells in the notebook

Now, simply run all the cells. The notebook should run successfully.

Google Colab Caveats

• The colab VM and python environment is ephemeral. The VM will reset after a while, all the installed libraries and files produced will be lost.
• Google Colab File IO. Download / Upload files to Google Colab
• You can connect a google drive to the colab notebook to read from and save to.
• Users can run the same colab notebook by sharing it with a link. This can have unwanted complications in case multiple people run the same cell at the same time.

Contact

• Amandeep Singh (amandeep@isi.edu)
• Pedro Szekely (pszekely@isi.edu)
• Filip Ilievski (ilievski@isi.edu)