Daniel V.

Thanks so much for mentioning Nariman A.! Lalla here's the tutorial from the Graph-based DS workshop, using kglab, pytorch geometric and Rubrix: https://docs.rubrix.ml/en/stable/tutorials/03-kglab_pytorch_geometric.html#%F0%9F%A7%AA-Node-classification-with-kglab-and-PyTorch-Geometric

Another area would be the intersection between Knowledge graphs and NLP, where you can use Rubrix for iterating on data for models such as relation classification, or named entity recognition and linking. All this, with a focus on involving subject matter experts, not only engineers or data scientists but also , which we believe is a key feature in the knowledge graph space.

Thank Ellie. There are several use cases related to knowledge graphs. One area would be related to knowledge graph construction and completion, where you have some sort of model or process for adding new entities, types or triples to a knowledge graph. In this area, Rubrix can be used for creating training data (in case you are using an ML approach) or curating the data to be added to the knowledge graph. During KGC we gave a tutorial related to this area, using kglab and Pytorch-geometric for adding types to an existing knowledge graph, here's the full tutorial and the use case description: https://docs.rubrix.ml/en/stable/tutorials/03-kglab_pytorch_geometric.html#Our-use-case-in-a-nutshell