===== Knowledge graph embeddings =====

  * Last verification: **20220909**
  * Tools required for this lab:
    * [[https://colab.research.google.com/|Google Colab]] or your own Python environment

==== Prepare yourself for the lab ====

  * //Advanced topics// lecture -- part about Graphs (metrics) and Graph Embeddings (slides are [[start#lectures|on the main page]])

==== Lab instructions ====

All instructions for this lab have been placed in the Jupyter Notebook: [[https://colab.research.google.com/drive/1upI7efXmShWwIXmr_4vKt5DZLjWsGOWH?usp=sharing|Knowledge graph embeddings]]

==== Learn more! ====

Graph libraries for Python:
  * [[https://graph-tool.skewed.de/|graph-tool]]
  * [[https://networkx.org/|NetworkX]]
  * [[https://www.timlrx.com/blog/benchmark-of-popular-graph-network-packages-v2|Benchmark of popular graph/network packages v2]] (2020)

Knowledge Graph Embeddings:
  * [[https://github.com/heathersherry/Knowledge-Graph-Tutorials-and-Papers/blob/master/topics/Knowledge%20Graph%20Embedding%2C%20Learning%2C%20Reasoning%2C%20Rule%20Mining%2C%20and%20Path%20Finding.md|Knowledge Graph Embedding, Learning, Reasoning, Rule Mining, and Path Finding]] -- github repository with a lot of papers and other materials
  * [[https://kge-tutorial-ecai2020.github.io/|Knowledge Graph Embeddings Tutorial: From Theory to Practice]] (2020) -- very deep, 3h long tutorial on KGE
  * [[https://www.mkbergman.com/cooking-with-python-and-kbpedia/|Cooking with Python and KBpedia series]] contains //Part VI: Applications and Machine Learning// with some embeddings-related stuff