Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of re-search areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.

Salatino, A., Osborne, F., Thanapalasingam, T., & Motta, E. (2019). The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles. In Digital Libraries for Open Knowledge. TPDL 2019 (pp.296-311). Springer Verlag [10.1007/978-3-030-30760-8_26].

The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles

Osborne F;
2019

Abstract

Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of re-search areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.
Si
paper
Scientifica
Bibliographic data; Digital libraries; Ontology; Scholarly data; Science of science; Text mining; Topic detection; Word embeddings;
English
23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019 - 9 September 2019 through 12 September 2019
978-3-030-30759-2
https://link.springer.com/chapter/10.1007/978-3-030-30760-8_26
Salatino, A., Osborne, F., Thanapalasingam, T., & Motta, E. (2019). The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles. In Digital Libraries for Open Knowledge. TPDL 2019 (pp.296-311). Springer Verlag [10.1007/978-3-030-30760-8_26].
Salatino, A; Osborne, F; Thanapalasingam, T; Motta, E
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/381229
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