Tutorials

For a comprehensive theoretical and hands-on overview of KGE models and hands-on AmpliGraph, check out our tutorials: COLING-22 KGE4NLP Tutorial (Slides + Colab Notebook) and ECAI-20 Tutorial (Slides + Recording + Colab Notebook).

The following Jupyter notebooks will guide you through the most important features of AmpliGraph:

• AmpliGraph basics: training, saving and restoring a model, evaluating a model, discover new links, visualize embeddings. [Jupyter notebook] [Colab notebook]

• Link-based clustering and classification: how to use the knowledge embeddings generated by a graph of international football matches in clustering and classification tasks. [Jupyter notebook] [Colab notebook]

Additional examples and code snippets are available here.

If you reuse materials presented in the tutorials, cite as:

@misc{kge4nlp_tutorial_coling22,
	title = {Knowledge Graph Embeddings for NLP: From Theory to Practice},
	url = {https://kge4nlp-coling22.github.io/},
    author= {Luca Costabello and
             Adrianna Janik and
             Eda Bayram and
             Sumit Pai},
	date = {2022-16-10},
    note = {COLING 2022 Tutorials}
}

@misc{kge_tutorial_ecai20,
	title = {Knowledge Graph Embeddings Tutorial: From Theory to Practice},
	url = {http://kge-tutorial-ecai-2020.github.io/},
    author= {Luca Costabello and
             Sumit Pai and
             Adrianna Janik and
             Nick McCarthy},
	shorttitle = {Knowledge Graph Embeddings Tutorial},
	date = {2020-09-04},
    note = {ECAI 2020 Tutorials}
}