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.

翻译：科学论文可划分为元数据和全文正文两大结构。元数据提供论文的简要概述，而全文正文则包含对研究人员具有重要价值的关键见解。知识获取是从科学论文中检索元数据和关键见解的核心活动，包括收集、分析和组织论文中蕴含的知识，使其能够在需要时被使用和重用。鉴于科学文献的丰富性，人工知识获取是一项繁琐的任务，因此通常采用计算机辅助和（半）自动化策略。本研究的目的一方面是使用与本体学习相关的论文来策展开放研究知识图谱（ORKG），另一方面是定义一种利用ORKG作为计算机辅助工具来组织从研究论文中提取的关键见解的方法。该方法已被用于记录"流行病监测系统设计与实现"研究问题并撰写本文的相关工作，目前正被用于记录"食品信息工程"、"表格数据到知识图谱匹配"及"问答"等研究问题以及"神经符号AI"领域。