  Coreference resolution is essential for automatic text understanding to
facilitate high-level information retrieval tasks such as text summarisation or
question answering. Previous work indicates that the performance of
state-of-the-art approaches (e.g. based on BERT) noticeably declines when
applied to scientific papers. In this paper, we investigate the task of
coreference resolution in research papers and subsequent knowledge graph
population. We present the following contributions: (1) We annotate a corpus
for coreference resolution that comprises 10 different scientific disciplines
from Science, Technology, and Medicine (STM); (2) We propose transfer learning
for automatic coreference resolution in research papers; (3) We analyse the
impact of coreference resolution on knowledge graph (KG) population; (4) We
release a research KG that is automatically populated from 55,485 papers in 10
STM domains. Comprehensive experiments show the usefulness of the proposed
approach. Our transfer learning approach considerably outperforms
state-of-the-art baselines on our corpus with an F1 score of 61.4 (+11.0),
while the evaluation against a gold standard KG shows that coreference
resolution improves the quality of the populated KG significantly with an F1
score of 63.5 (+21.8).
