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

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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New release: CSO Classifier v2.1

We are pleased to announce that we recently created a new release of the CSO Classifier (v2.1), an application for automatically classifying research papers according to the Computer Science Ontology (CSO). Recently, we have been intensively working on improving its scalability, removing all its bottlenecks and making sure it could be run on large corpus. […]

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CSO Classifier

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 page, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according […]

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Classifying Research Papers with the Computer Science Ontology

“Classifying Research Papers with the Computer Science Ontology” is a demo paper submitted to the International Semantic Web Conference (ISWC) 2018 , 8-12 October 2018, Monterey, California, USA, 2018. Poster DOI. 10.21954/ou.rd.7204814 Poster paper PDF. http://oro.open.ac.uk/55908/ Code: Authors. Angelo A. Salatino, Thiviyan Thanapalasingam, Andrea Mannocci, Francesco Osborne, Enrico Motta Abstract. Ontologies of research areas are important tools for characterising, exploring […]

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