As part of the H2020 GATEKEEPER, we are developing an App with the SME SPIROCCO to support community-based care. SPIROCCO is a winner of €50k funds of the 1st GK Open Call, addressing our challenge of developing a web technology to fight loneliness and strengthening of community resilience. The APP will enable citizens to request […]
The Conference Dashboard (also known as AIDA Dashboard) is a tool for exploring and making sense of scientific conferences which integrates statistical analysis, semantic technologies, and visual analytics. The dashboard was developed in collaboration with Springer Nature for assisting editors in assessing conferences, but it also supports several other use cases. Compared to other state-of-the-art […]
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 […]
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 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 […]
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 […]
Augur is a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Augur operates in three steps. First, it creates evolutionary networks describing the collaboration between research topics over […]
The Open University team and Springer Nature have launched a new research project with the aim of developing new innovative solutions to support editorial activities at Springer Nature. The project will be lead by Enrico Motta and Francesco Osborne and will start on May 2018. The SKM3 team and Springer Nature have been collaborating since 2015 […]
Rexplore leverages novel solutions in large-scale data mining, semantic technologies and visual analytics, to provide an innovative environment for exploring and making sense of scholarly data. In particular, Rexplore allows users: To detect and make sense of important trends in research, such as, significant migrations of researchers from one area to another, the emergence of […]
The Smart Book Recommender(SBR) is semantic application designed to support the Springer Nature editorial team in promoting their publications at Computer Science venues. It takes as input the proceedings of a conference and suggests books, journals, and other conference proceedings which are likely to be relevant to the attendees of the conference in question. It does […]
Scholarly Knowledge Mining
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Smart Cities and Robotics
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