  A scientific paper can be divided into two major constructs which are
Metadata and Full-body text. Metadata provides a brief overview of the paper
while the Full-body text contains key-insights that can be valuable to fellow
researchers. To retrieve metadata and key-insights from scientific papers,
knowledge acquisition is a central activity. It consists of gathering,
analyzing and organizing knowledge embedded in scientific papers in such a way
that it can be used and reused whenever needed. Given the wealth of scientific
literature, manual knowledge acquisition is a cumbersome task. Thus,
computer-assisted and (semi-)automatic strategies are generally adopted. Our
purpose in this research was two fold: curate Open Research Knowledge Graph
(ORKG) with papers related to ontology learning and define an approach using
ORKG as a computer-assisted tool to organize key-insights extracted from
research papers. This approach was used to document the "epidemiological
surveillance systems design and implementation" research problem and to prepare
the related work of this paper. It is currently used to document "food
information engineering", "Tabular data to Knowledge Graph Matching" and
"Question Answering" research problems and "Neuro-symbolic AI" domain.
