Category Archives: Technology

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 AIDA Dashboard

The 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 solutions, it introduces three novel […]

Supporting editorial activities at Springer Nature

The SKM3 team at the OU’s Knowledge Media Institute and Springer Nature have signed a new agreement, extending their collaboration in the area of scholarly analytics until January 2021. The collaboration between the SKM3 team and Springer Nature started in 2014 and has continued without interruptions for the past 5 years. This partnership focuses on […]

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 […]

Augur – Early Forecasting of Research Trends

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 […]


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 […]

Smart Book Recommender

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 […]

Klink-2: Automatic generation of large scale taxonomies of research areas

Klink-2 is an application which takes as input large amounts of scholarly metadata and automatically generates an OWL ontology containing all the research areas mined from the input data and their semantic relationships. It was developed to produced large scale ontology of research topics.   The traditional way to address the problem of identifying and […]