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

  • Angelo A. SalatinoEmail author
  • Francesco Osborne
  • Thiviyan Thanapalasingam
  • Enrico Motta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11799)

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.

Keywords

Scholarly data Digital libraries Bibliographic data Ontology Text mining Topic detection Word embeddings Science of science 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Angelo A. Salatino
    • 1
    Email author
  • Francesco Osborne
    • 1
  • Thiviyan Thanapalasingam
    • 1
  • Enrico Motta
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK

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