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.

Aggarwal, T., Salatino, A., Osborne, F., Motta, E. (2022). R-classify: Extracting research papers’ relevant concepts from a controlled vocabulary. SOFTWARE IMPACTS, 14(December 2022) [10.1016/j.simpa.2022.100444].

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

Osborne F.;
2022

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.
Articolo in rivista - Articolo scientifico
Scholarly data; Scholarly ontologies; Science of science; Text mining; Topic detection; Topic extraction;
English
15-nov-2022
2022
14
December 2022
100444
open
Aggarwal, T., Salatino, A., Osborne, F., Motta, E. (2022). R-classify: Extracting research papers’ relevant concepts from a controlled vocabulary. SOFTWARE IMPACTS, 14(December 2022) [10.1016/j.simpa.2022.100444].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/412295
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