  We introduce a novel task, called Generalized Relation Discovery (GRD), for
open-world relation extraction. GRD aims to identify unlabeled instances in
existing pre-defined relations or discover novel relations by assigning
instances to clusters as well as providing specific meanings for these
clusters. The key challenges of GRD are how to mitigate the serious model
biases caused by labeled pre-defined relations to learn effective relational
representations and how to determine the specific semantics of novel relations
during classifying or clustering unlabeled instances. We then propose a novel
framework, SFGRD, for this task to solve the above issues by learning from
semi-factuals in two stages. The first stage is semi-factual generation
implemented by a tri-view debiased relation representation module, in which we
take each original sentence as the main view and design two debiased views to
generate semi-factual examples for this sentence. The second stage is
semi-factual thinking executed by a dual-space tri-view collaborative relation
learning module, where we design a cluster-semantic space and a class-index
space to learn relational semantics and relation label indices, respectively.
In addition, we devise alignment and selection strategies to integrate two
spaces and establish a self-supervised learning loop for unlabeled data by
doing semi-factual thinking across three views. Extensive experimental results
show that SFGRD surpasses state-of-the-art models in terms of accuracy by
2.36\% $\sim$5.78\% and cosine similarity by 32.19\%$\sim$ 84.45\% for relation
label index and relation semantic quality, respectively. To the best of our
knowledge, we are the first to exploit the efficacy of semi-factuals in
relation extraction.
