The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

“The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas” is a journal paper submitted to the Special Issue on Best Resources papers of the Data Intelligence Jornal (MIT Press). Authors Angelo A. Salatino1, Thiviyan Thanapalasingam1, Andrea Mannocci1, Aliaksandr Birukou2, Francesco Osborne1, Enrico Motta1 1 Knowledge Media Institute, The Open University, MK7 6AA, Milton Keynes, UK 2 […]

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Integrating Knowledge Graphs for Comparing the Scientific Output of Academia and Industry

“Integrating Knowledge Graphs for Comparing the Scientific Output of Academia and Industry” is a poster paper submitted to the poster and demo session of the International Semantic Web Conference, October 26 – 30, 2019 The University of Auckland, New Zealand. Authors Simone Angioni1, Francesco Osborne2, Angelo A. Salatino2, Diego Reforgiato Recupero1, Enrico Motta2 1 University of Cagliari, […]

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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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Computer Science Ontology

The Computer Science Ontology is a large-scale ontology of research areas that was automatically generated using the Klink-2 algorithm [1] on a dataset of about 16 million publications, mainly in the field of Computer Science. In the rest of the paper, we will refer to this corpus as the Rexplore dataset [2]. The current version of CSO […]

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