  Embedding-based methods for reasoning in knowledge hypergraphs learn a
representation for each entity and relation. Current methods do not capture the
procedural rules underlying the relations in the graph. We propose a simple
embedding-based model called ReAlE that performs link prediction in knowledge
hypergraphs (generalized knowledge graphs) and can represent high-level
abstractions in terms of relational algebra operations. We show theoretically
that ReAlE is fully expressive and provide proofs and empirical evidence that
it can represent a large subset of the primitive relational algebra operations,
namely renaming, projection, set union, selection, and set difference. We also
verify experimentally that ReAlE outperforms state-of-the-art models in
knowledge hypergraph completion, and in representing each of these primitive
relational algebra operations. For the latter experiment, we generate a
synthetic knowledge hypergraph, for which we design an algorithm based on the
Erdos-R'enyi model for generating random graphs.
