Tag Archives: Scholarly Knowledge Mining

The CSO Classifier nominated for the Best Paper Award

Last week, Angelo was in Oslo for the 23rd edition of the International Conference on Theory and Practice of Digital Libraries (TPDL2019). Every year, this conference attracts many researchers from all over the world, working in the fields of digital libraries, information retrieval, text analysis, web archives, and many others. Angelo attended the conference to present […]

SMART TOPIC MINER – Improving Editorial Workflow and Metadata Quality at Springer Nature

Smart Topic Miner Smart Topic Miner (STM) is an application that assists the Springer Nature editorial team in classifying the scientific literature in Computer Science in terms of a catalogue of about 15K research topics, with a very high degree of accuracy. This catalogue was itself generated by our team using a highly innovative knowledge […]

CSO-Classifier available on PIP

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

Angelo succeeds in his VIVA

Congratulations to Angelo Salatino for succeeding in his viva! On 31st May 2019, Angelo Salatino successfully defended his PhD thesis on Early Detection of Research Trends. The thesis is a body of research work over the last three years leading to a successful system that identifies the emergence of new research topics up to two […]

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. […]

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 […]

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 […]

Runner-Up at Springer Nature Hack Day in London

On the 29th November 2017, Angelo, Andrea and Thiviyan attended the second edition of SpringerNature HackDay in London (@ SpringerNature Campus). Aliaksandr Birukou, Executive Editor of Computer Science at Springer Nature and collaborator of our research team at the Knowledge Media Institute, also joined our group on the HackDay. The whole event aimed at joining together the […]

Best Paper Award at SAVE-SD 2016 (WWW2016)

On the 11th of April 2016, Angelo and Enrico won the Best Paper Award at the Workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Data (SAVE-SD 2016) of the 25th International World Wide Web Conference (WWW 2016) for the paper Detection of Embryonic Research Topics by Analysing Semantic Topic Networks. Just got the Best Paper Award at #savesd2016 .. […]