Predicting Missing Links Using PyKEEN
Mehdi Ali, Charles Tapley Hoyt, Daniel Domingo‐Fernándéz, Jens Lehmann · Fraunhofer-Publica (Fraunhofer-Gesellschaft) · 2022
PyKEEN is a framework, which integrates several approaches to compute knowledge graph embeddings (KGEs). We demonstrate the usage of PyKEEN in an biomedical use case, i.e. we trained and evaluated several KGE models on a biological knowledge graph containing genes annotations to pathways and pathway hierarchies from well-known databases. We used the best performing model to predict new links and present an evaluation in collaboration with a domain expert.