  Recent transformer-based approaches demonstrate promising results on
relational scientific information extraction. Existing datasets focus on
high-level description of how research is carried out. Instead we focus on the
subtleties of how experimental associations are presented by building SciClaim,
a dataset of scientific claims drawn from Social and Behavior Science (SBS),
PubMed, and CORD-19 papers. Our novel graph annotation schema incorporates not
only coarse-grained entity spans as nodes and relations as edges between them,
but also fine-grained attributes that modify entities and their relations, for
a total of 12,738 labels in the corpus. By including more label types and more
than twice the label density of previous datasets, SciClaim captures causal,
comparative, predictive, statistical, and proportional associations over
experimental variables along with their qualifications, subtypes, and evidence.
We extend work in transformer-based joint entity and relation extraction to
effectively infer our schema, showing the promise of fine-grained knowledge
graphs in scientific claims and beyond.
