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 developing intelligent solutions that capitalise on the research work of the SKM3 team to improve both the quality and the efficiency of the editorial process at Springer Nature and to allow editors to make informed, evidence-based decisions about their marketing strategy.

Since 2014, this partnership has developed a number of innovative technologies, including:

  1. A portal allowing users to browse, download, and provide feedback about the Computer Science Ontology (CSO), the world’s largest taxonomy of research areas in Computer Science, which is automatically generated and regularly updated using the Klink-2 algorithm over a very large corpus of papers in Computer Science. CSO, which has been formally adopted by Springer Nature, includes 14K topics and 163K relationships and provides the ontological basis for all solutions deployed by the SKM3 team.
  2. The CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO). The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research topics drawn from the ontology.
  3. Smart Topic Miner, a tool which uses semantic web technologies to classify the conference proceedings in Computer Science published by Springer Nature, about 800 volumes a year, in terms of the research topics provided by the CSO ontology. A demo of the system is available at
  4. Smart Book Recommender (SBR), which assists editors in deciding which editorial products to market at a specific conference venue. SBR takes as input data about a conference and suggests books, journals, and other conference proceedings which are likely to be relevant to the attendees of the conference in question. A SBR demo is available at

Building on this extensive set of innovative technologies, we plan to further develop our collaboration in the next year by developing both new analytics tools for Computer Science editors, as well as new intelligence capabilities, to support the Marketing department in tailoring their product packages for different organizations.

We are very excited to renew our longstanding strategic partnership with Springer Nature and we are very much looking forward to producing a new generation of intelligent solutions to support their editorial and marketing teams.

Relevant papers

Overview of collaboration with Springer Nature

Computer Science Ontology

CSO Classifier

Smart Topic Miner

Smart Book Recommender

Klink algorithm