Are Relational Triple Extraction Frameworks Sufficient for Hyper-relational Facts ?

AbstractHyper-relational fact extraction involves identifying relational triples along with additional contextual information—known as qualifiers—such as time, location, or quantity. These qualifiers enable models to represent complex real-world knowledge more accurately. While numerous end-to-end models have been developed for extracting relational triples, they are not designed to handle qualifiers directly. In this work, we propose a straightforward and effective approach to extend existing end-to-end triple extraction models to also capture qualifiers. Our method reformulates qualifiers as new relations by computing the Cartesian product between qualifiers and their associated relations. This transformation allows the model to extract qualifier information as additional triples, which can later be merged to form complete hyper-relational facts. We evaluate our approach using multiple end-to-end triple extraction models on the HyperRED dataset and demonstrate its effectiveness in extracting hyper-relational facts.