Tag Archives: skm3

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 […]

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

Abstract. 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 […]

The Open University and Springer Nature launch the Computer Science Ontology

The Knowledge Media Institute (KMi) of The Open University and Springer Nature are partnering to provide a comprehensive Computer Science Ontology (CSO) to a broad range of communities engaged with scholarly data. CSO can be accessed free of charge through the CSO Portal, a web application that enables users to download, explore, and provide feedback […]

SKM3 at ISWC 2018

Every year the International Semantic Web Conference (ISWC) is the main destination for many researchers in the Semantic Web community. The 17th edition of the conference was held last week at the Asilomar Conference Grounds in Monterey, California, and it hosted around 500 researchers coming from all around the world.This year the SKM3 presented three full papers, covering all the main tracks, […]

AUGUR presented at JCDL 2018

The ACM/IEEE Joint Conference on Digital Libraries in 2018 (JCDL 2018) took place last week in Fort Worth (Texas). Angelo attended the conference to discuss his recent advances showed in the research paper “AUGUR: Forecasting the Emergence of New Research Topics”. In brief, Augur is a framework analysing the diachronic relationships between research areas and […]

Great success for the SKM3 team at ISWC 2018

The SKM3 team, the KMi research group on scholarly analytics, will present three full papers at the 2018 International Semantic Web Conference (ISWC), the premiere international venue for the Semantic Web and Linked Data communities, which will be held in October in Monterey, California. The SKM3 team succeeded in having a paper accepted in each […]

Runner-Up at Springer Nature Hack Day in Berlin

On 26-27 April 2018, Angelo and Francesco attended the third edition of the Springer Nature Hack Day, which was held in its headquarter in Berlin. The Springer Nature Hack Day is an event that allows researchers, developers, tech companies, and Springer Nature itself, to gather together and tackle current research issues. Offering also opportunities for potential […]

Best Paper Award at SAVE-SD 2018

The SKM³ team is proud to announce that the paper “Geographical trends in research: a preliminary analysis on authors’ affiliations” presented by Andrea at the workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Dissemination (SAVE-SD) held at The Web Conference 2018 in Lyon has been awarded the Best Paper Award. We thank the organising committee and Springer […]

The Computer Science Ontology (CSO)

The Computer Science Ontology (CSO) is a large-scale ontology of research areas that was automatically generated using the Klink-2 algorithm [1] on the Rexplore dataset [2], which consists of about 16 million publications, mainly in the field of Computer Science. The Klink-2 algorithm combines semantic technologies, machine learning, and knowledge from external sources to automatically […]

Technology-Topic Framework

The Technology-Topic Framework (TTF) is a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. TTF characterizes technologies in terms of a set of topics drawn from a large-scale ontology of research areas over a given time period and applies machine learning on these data to forecast […]