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and ontologies of research topics are key to this process, but manually creating them is costly and often leads to outdated results.This poster paper shows the use of SciBERT model to automatically generate research topic ontologies.Our model excels at identifying semantic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.

Classifying Scientific Topic Relationships with SciBERT

Pompianu L.;Riboni D.;reforgiato Recupero D.
2024-01-01

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and ontologies of research topics are key to this process, but manually creating them is costly and often leads to outdated results.This poster paper shows the use of SciBERT model to automatically generate research topic ontologies.Our model excels at identifying semantic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.
2024
Knowledge Graph Generation
Language Models
Ontology Generation
Research Topics
SciBERT
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/426487
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