  Relation extraction has the potential for large-scale knowledge graph
construction, but current methods do not consider the qualifier attributes for
each relation triplet, such as time, quantity or location. The qualifiers form
hyper-relational facts which better capture the rich and complex knowledge
graph structure. For example, the relation triplet (Leonard Parker, Educated
At, Harvard University) can be factually enriched by including the qualifier
(End Time, 1967). Hence, we propose the task of hyper-relational extraction to
extract more specific and complete facts from text. To support the task, we
construct HyperRED, a large-scale and general-purpose dataset. Existing models
cannot perform hyper-relational extraction as it requires a model to consider
the interaction between three entities. Hence, we propose CubeRE, a
cube-filling model inspired by table-filling approaches and explicitly
considers the interaction between relation triplets and qualifiers. To improve
model scalability and reduce negative class imbalance, we further propose a
cube-pruning method. Our experiments show that CubeRE outperforms strong
baselines and reveal possible directions for future research. Our code and data
are available at github.com/declare-lab/HyperRED.
