Classifying Scientific Topic Relationships with SciBERT

Pisu, Alessia; Pompianu, Livio; Salatino, Angelo; Osborne, Francesco; Riboni, Daniele; Motta, Enrico and Reforgiato Recupero, Diego (2024). Classifying Scientific Topic Relationships with SciBERT. In: Joint Proc. of Posters, Demos, Workshops, and Tutorials of the 20th Int.l Conf. on Semantic Systems (SEMANTiCS 2024), 17-19 Sep 2024, Amsterdam, CEUR-WS.

URL: https://ceur-ws.org/Vol-3759/paper14.pdf

Abstract

Current AI systems, including smart search engines and recommendation systems tools for streamlining literature reviews, and interactive question-answering platforms, are becoming indispensable for researchers to navigate and understand the vast landscape of scientific knowledge. Taxonomies and ontologies of research topics are key to this process, but manually creating them is costly and often leads to outdated results. This poster paper shows the use of SciBERT model to automatically generate research topic ontologies. Our model excels at identifying semantic relationships between research topics, outperforming traditional methods. This approach promises to streamline the creation of accurate and up-to-date ontologies, enhancing the effectiveness of AI tools for researchers.

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