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There is an emerging trend of embedding knowledge graphs (KGs) in continuousvector spaces in order to use those for machine learning tasks. Recently, manyknowledge graph embedding (KGE) models have been proposed that learn lowdimensional representations while trying to maintain the structural propertiesof the KGs such as the similarity of nodes depending on their edges to othernodes. KGEs can be used to address tasks within KGs such as the prediction ofnovel links and the disambiguation of entities. They can also be used fordownstream tasks like question answering and fact-checking. Overall, thesetasks are relevant for the semantic web community. Despite their popularity,the reproducibility of KGE experiments and the transferability of proposed KGEmodels to research fields outside the machine learning community can be a majorchallenge. Therefore, we present the KEEN Universe, an ecosystem for knowledgegraph embeddings that we have developed with a strong focus on reproducibilityand transferability. The KEEN Universe currently consists of the Pythonpackages PyKEEN (Python KnowlEdge EmbeddiNgs), BioKEEN (Biological KnowlEdgeEmbeddiNgs), and the KEEN Model Zoo for sharing trained KGE models with thecommunity.

The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability