  Knowledge graphs store facts using relations between two entities. In this
work, we address the question of link prediction in knowledge hypergraphs where
relations are defined on any number of entities. While techniques exist (such
as reification) that convert non-binary relations into binary ones, we show
that current embedding-based methods for knowledge graph completion do not work
well out of the box for knowledge graphs obtained through these techniques. To
overcome this, we introduce HSimplE and HypE, two embedding-based methods that
work directly with knowledge hypergraphs. In both models, the prediction is a
function of the relation embedding, the entity embeddings and their
corresponding positions in the relation. We also develop public datasets,
benchmarks and baselines for hypergraph prediction and show experimentally that
the proposed models are more effective than the baselines.
