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

Salatino, Angelo; Osborne, Francesco; Thanapalasingam, Thiviyan and Motta, Enrico (2019). The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles. In: TPDL 2019: 23rd International Conference on Theory and Practice of Digital Libraries, Lecture Notes in Computer Science, Springer.

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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.

Item Type: Conference or Workshop Item
Keywords: Scholarly Data; Digital Libraries; Bibliographic Data; Ontology; Text Mining; Topic Detection; Word Embeddings; Science of Science
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 62026
Depositing User: Angelo Salatino
Date Deposited: 21 Jun 2019 14:19
Last Modified: 01 Jul 2019 17:33
URI: http://oro.open.ac.uk/id/eprint/62026
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