  We present MMKG, a collection of three knowledge graphs that contain both
numerical features and (links to) images for all entities as well as entity
alignments between pairs of KGs. Therefore, multi-relational link prediction
and entity matching communities can benefit from this resource. We believe this
data set has the potential to facilitate the development of novel multi-modal
learning approaches for knowledge graphs.We validate the utility ofMMKG in the
sameAs link prediction task with an extensive set of experiments. These
experiments show that the task at hand benefits from learning of multiple
feature types.
