R-classify: Extracting research papers’ relevant concepts from a controlled vocabulary

Aggarwal, Tanay; Salatino, Angelo; Osborne, Francesco and Motta, Enrico (2022). R-classify: Extracting research papers’ relevant concepts from a controlled vocabulary. Software Impacts, 14, article no. 100444.

DOI: https://doi.org/10.1016/j.simpa.2022.100444

Abstract

In the past few decades, we saw a proliferation of scientific articles available online. This data-rich environment offers several opportunities but also challenges, since it is problematic to explore these resources and identify all the relevant content. Hence, it is crucial that they are appropriately annotated with their relevant concepts so to increase their chance of being properly indexed and retrieved. In this paper, we present R-Classify, a web tool that assists users in identifying the most relevant concepts according to a large-scale ontology of research areas in the field of Computer Science.

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• Item ORO ID
• 85958
• Item Type
• Journal Item
• ISSN
• 2665-9638
• Keywords
• Topic detection; Topic extraction; Scholarly data; Science of science; Text mining; Scholarly ontologies
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM)
  Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
• Copyright Holders
• © 2022 The Authors
• Depositing User
• ORO Import