Ontologies play a critical role in Semantic Web technologies by providing a
structured and standardized way to represent knowledge and enabling machines to
understand the meaning of data. Several taxonomies and ontologies have been
generated, but individuals target one domain, and only some of those have been
found expensive in time and manual effort. Also, they need more coverage of
unconventional topics representing a more holistic and comprehensive view of
the knowledge landscape and interdisciplinary collaborations. Thus, there needs
to be an ontology covering Science and Technology and facilitate
multidisciplinary research by connecting topics from different fields and
domains that may be related or have commonalities. To address these issues, we
present an automatic Science and Technology Ontology (S&TO) that covers
unconventional topics in different science and technology domains. The proposed
S&TO can promote the discovery of new research areas and collaborations across
disciplines. The ontology is constructed by applying BERTopic to a dataset of
393,991 scientific articles collected from Semantic Scholar from October 2021
to August 2022, covering four fields of science. Currently, S&TO includes 5,153
topics and 13,155 semantic relations. S&TO model can be updated by running
BERTopic on more recent datasets

该研究介绍了一个包含科学技术领域中非传统主题的自动化科学技术本体，并通过连接不同领域和专业之间相关或具有共性的主题来促进跨学科研究与合作。使用从 2021 年 10 月到 2022 年 8 月从语义学者 (Semantic Scholar) 收集的 393,991 篇科学文章构建 S＆TO。目前，S&TO 划分了四个科学领域中的 5153 个主题和 13155 个语义关系，可以通过对更新的数据集运行 BERTopic 来更新模型。