Domain experts in various fields have formalised their knowledge in ontologies. These are especially present in biomedical sciences where such controlled vocabularies are also used to annotate the data in various databases. This means that knowledge graphs with information from various sources can be formed, accompanied by ontologies describing the classes used and grounding the problem in knowledge acquired over years of research. This project will investigate neuro-symbolic methods to learn and predict from knowledge graphs, e.g., through learning embeddings adhering to the rules stated in the ontologies. By utilising such rules search spaces can be reduced, asserting predictions are viable, as well as making the models and predictions more interpretable by grounding them in semantic meaningful representations. These embeddings will be combined with graph neural networks (GNNs) for prediction of links and properties in the graph. Apart from developing neuro-symbolic approaches for knowledge graph learning, this project will aim to help scientific discoveries about yeast. Partly through predictions from the graph, and partly by using this to help forming hypotheses and evaluating finding from automated Robot Scientists. This can, e.g., be done by predicting missing information in the graph or measuring similarities by new findings and the currently represented knowledge.