This tutorial introduces graph-based data science work, where machine learning approaches can be combined with complementary knowledge graph work. The tutorials leverage a popular library `kglab` – an open source project that integrates RDFlib, OWL-RL, pySHACL, NetworkX, iGraph, pslpython, node2vec, PyVis, and more – to show how to use a wide range of graph-based approaches, blending smoothly into data science workflows, and working efficiently with popular data engineering practices.
Within this space of open source graph libraries in Python, there are several camps: semantic graphs, probabilistic graphs, graph algorithms, graph ML, interactive visualization, etc. Previously these "camps" did not collaborate much and the libraries were difficult to integrate. We'll show how to write brief Python code to build complementary "Hybrid AI" workflows, which is ideal for strategies such as self-supervised learning. All of the training material is available as Jupyter notebooks.
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