  In natural language, often multiple entities appear in the same text.
However, most previous works in Relation Extraction (RE) limit the scope to
identifying the relation between two entities at a time. Such an approach
induces a quadratic computation time, and also overlooks the interdependency
between multiple relations, namely the relation of relations (RoR). Due to the
significance of RoR in existing datasets, we propose a new paradigm of RE that
considers as a whole the predictions of all relations in the same context.
Accordingly, we develop a data-driven approach that does not require
hand-crafted rules but learns by itself the RoR, using Graph Neural Networks
and a relation matrix transformer. Experiments show that our model outperforms
the state-of-the-art approaches by +1.12\% on the ACE05 dataset and +2.55\% on
SemEval 2018 Task 7.2, which is a substantial improvement on the two
competitive benchmarks.
