Leveraging Language Models for Generating Ontologies of Research Topics

Pisu, Alessia; Pompianu, Livio; Salatino, Angelo; Osborne, Francesco; Riboni, Daniele; Motta, Enrico and Recupero, Diego Reforgiato (2024). Leveraging Language Models for Generating Ontologies of Research Topics. In: Text2KG 2024: International Workshop on Knowledge Graph Generation from Text, 28 May 2024, Hersonissos, Crete, Greece.

URL: https://ceur-ws.org/Vol-3747/text2kg_paper6.pdf

Abstract

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 However, creating these classifications manually is an expensive and time-consuming process, often resulting in outdated and coarse-grained representations.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.

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