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, pp. 296–311.

DOI: https://doi.org/10.1007/978-3-030-30760-8_26

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.

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• Item ORO ID
• 62026
• Item Type
• Conference or Workshop Item
• ISSN
• 978-3-030-30760-8
• Keywords
• Scholarly Data; Digital Libraries; Bibliographic Data; Ontology; Text Mining; Topic Detection; Word Embeddings; Science of Science
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Related URLs
• • http://www.tpdl.eu/tpdl2019/
• Depositing User
• Angelo Salatino