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Publication . Part of book or chapter of book . Article . Preprint . 2019

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

Angelo Antonio Salatino; Francesco Osborne; Thiviyan Thanapalasingam; Enrico Motta;
Open Access

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.

Conference paper at TPDL 2019

Subjects by Vocabulary

Microsoft Academic Graph classification: Variety (cybernetics) Field (computer science) Metadata Computer science Ontology (information science) Information retrieval Classifier (UML) Analytics business.industry business Selection (linguistics) Retrievability


Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Digital Libraries, Information Retrieval (cs.IR), Artificial Intelligence (cs.AI), Digital Libraries (cs.DL), FOS: Computer and information sciences

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