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.

使用计算机辅助工具的知识获取，构建 Open Research Knowledge Graph 并整理自科研论文中提取的关键见解。

基于开放研究知识图谱的科学论文知识获取方法

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

Background: Recent years are seeing a growing impetus in the semantification
of scholarly knowledge at the fine-grained level of scientific entities in
knowledge graphs. The Open Research Knowledge Graph (ORKG)
this https URL represents an important step in this direction, with
thousands of scholarly contributions as structured, fine-grained,
machine-readable data. There is a need, however, to engender change in
traditional community practices of recording contributions as unstructured,
non-machine-readable text. For this in turn, there is a strong need for AI
tools designed for scientists that permit easy and accurate semantification of
their scholarly contributions. We present one such tool, ORKG-assays.
Implementation: ORKG-assays is a freely available AI micro-service in ORKG
written in Python designed to assist scientists obtain semantified bioassays as
a set of triples. It uses an AI-based clustering algorithm which on
gold-standard evaluations over 900 bioassays with 5,514 unique property-value
pairs for 103 predicates shows competitive performance. Results and Discussion:
As a result, semantified assay collections can be surveyed on the ORKG platform
via tabulation or chart-based visualizations of key property values of the
chemicals and compounds offering smart knowledge access to biochemists and
pharmaceutical researchers in the advancement of drug development.

该论文介绍了 ORKG-assays 工具，一个帮助科学家将他们的学术贡献语义化的 AI 工具，它利用了 AI 聚类算法，可以帮助药学研究人员在药物开发方面智能地访问语义化的生物分析集合。