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. […]

Read More

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 […]

Read More

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 […]

Read More

AUGUR: Forecasting the Emergence of New Research Topics

“AUGUR: Forecasting the Emergence of New Research Topics” is a paper submitted to the ACM/IEEE Joint Conference on Digital Libraries 2018, presented on June 5 2018, in Fort Worth, TX, USA Authors Angelo Salatino, Francesco Osborne and Enrico Motta Abstract Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional […]

Read More

Smart Topic Miner

Smart Topic Miner (STM) is a web application which uses Semantic Web technologies to classify scholarly publications on the basis of Computer Science Ontology (CSO), a very large automatically generated ontology of research areas.   STM was developed to support the Springer Nature Computer Science editorial team in classifying proceedings in the LNCS family. It […]

Read More