“The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles” is a full paper submitted to the TPDL 2019: 23rd International Conference on Theory and Practice of Digital Libraries, 9-12 September 2019 OsloMet – Oslo Metropolitan University, Oslo, Norway
Angelo A. Salatino, Francesco Osborne, Thiviyan Thanapalasingam, Enrico Motta
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 research 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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