Leveraging Language Models for Generating Ontologies of Research Topics

The current generation of artificial intelligence technologies, such as smart search engines, recommendation systems, tools for systematic reviews, and question-answering applications, plays a crucial role in helping researchers manage and interpret scientific literature. Taxonomies and ontologies of research topics are a fundamental part of this environment as they allow intelligent systems and scientists to navigate the ever-growing number of research papers. However, creating these classifications manually is an expensive and time-consuming process, often resulting in outdated and coarse-grained representations. Consequently, researchers have been focusing on developing automated or semi-automated methods to create taxonomies of research topics. This paper studies the application of transformer-based language models for generating research topic ontologies. Specifically, we have developed a model leveraging SciBERT to identify four semantic relationships between research topics (supertopic, subtopic, same-as, and other) and conducted a comparative analysis against alternative solutions. The preliminary findings indicate that the transformer-based model significantly surpasses the performance of models reliant on traditional features.