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A scientific paper can be divided into two major constructs which areMetadata and Full-body text. Metadata provides a brief overview of the paperwhile the Full-body text contains key-insights that can be valuable to fellowresearchers. 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 waythat it can be used and reused whenever needed. Given the wealth of scientificliterature, manual knowledge acquisition is a cumbersome task. Thus,computer-assisted and (semi-)automatic strategies are generally adopted. Ourpurpose in this research was two fold: curate Open Research Knowledge Graph(ORKG) with papers related to ontology learning and define an approach usingORKG as a computer-assisted tool to organize key-insights extracted fromresearch papers. This approach was used to document the "epidemiologicalsurveillance systems design and implementation" research problem and to preparethe related work of this paper. It is currently used to document "foodinformation engineering", "Tabular data to Knowledge Graph Matching" and"Question Answering" research problems and "Neuro-symbolic AI" domain.

An approach based on Open Research Knowledge Graph for Knowledge Acquisition from scientific papers