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Publication . Part of book or chapter of book . Article . Preprint . 2019 . Embargo end date: 01 Jan 2021

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.

Comment: Conference paper at TPDL 2019

Subjects by Vocabulary

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


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

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