  There is an emerging trend of embedding knowledge graphs (KGs) in continuous
vector spaces in order to use those for machine learning tasks. Recently, many
knowledge graph embedding (KGE) models have been proposed that learn low
dimensional representations while trying to maintain the structural properties
of the KGs such as the similarity of nodes depending on their edges to other
nodes. KGEs can be used to address tasks within KGs such as the prediction of
novel links and the disambiguation of entities. They can also be used for
downstream tasks like question answering and fact-checking. Overall, these
tasks are relevant for the semantic web community. Despite their popularity,
the reproducibility of KGE experiments and the transferability of proposed KGE
models to research fields outside the machine learning community can be a major
challenge. Therefore, we present the KEEN Universe, an ecosystem for knowledge
graph embeddings that we have developed with a strong focus on reproducibility
and transferability. The KEEN Universe currently consists of the Python
packages PyKEEN (Python KnowlEdge EmbeddiNgs), BioKEEN (Biological KnowlEdge
EmbeddiNgs), and the KEEN Model Zoo for sharing trained KGE models with the
community.
