Science and Technology Ontology: A Taxonomy of Emerging Topics

TL;DR

This paper introduces an automatic Science and Technology Ontology (S&TO) built from a large dataset of scientific articles, aiming to enhance multidisciplinary research and discovery of emerging topics.

Contribution

The paper presents a novel, automated ontology covering diverse scientific fields, constructed using BERTopic on a large-scale dataset, reducing manual effort and increasing coverage of interdisciplinary topics.

Findings

S&TO includes 5,153 topics and 13,155 relations.

The ontology can be updated with new data using BERTopic.

It facilitates discovery of new research areas and collaborations.

Abstract

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