Supporting Editorial Activities at Springer Nature

The project aims at fostering Springer Nature editorial activities by supporting them with a variety of smart solutions leveraging artificial intelligence, data mining, and semantic technologies. In particular, the KMi team will support Springer Nature editorial team in classifying proceedings and other editorial products, taking informed decisions about their marketing strategy, and improve their internal classification.

Our team and Springer Nature have been collaborating since 2015 in the development of an array of semantically-enhanced solutions supporting editors in i) classifying proceedings and other editorial products with respect to the relevant research areas and ii) taking informed decisions about their marketing strategy. These solutions include:

  1. The creation of a portal for releasing the Computer Science Ontology (CSO), a large-scale ontology of research areas that was automatically generated using the Klink-2 algorithm on the Rexplore dataset. The portal will enable to browse, download, and offer feedback on CSO. Currently, we provide two versions of CSO: CSO 1.0 (stable, 15k topics and 96k relationships), and CSO 2.0 (last version, 26k topics, 226k relationships). You can browse CSO on the CSO Portal.
  2. The CSO Classifier is a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of research areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. A web application for trying the CSO Classifier is available at
  3. The development of Smart Topic Miner, a tool which uses semantic web technologies to classify scholarly publications on the basis of a very large automatically generated ontology of research areas. It was developed to support the Springer Nature Computer Science editorial team in classifying proceedings. A demo of the system is available at
  4. The Smart Book Recommender, which assists editors in deciding which editorial products should be marketed in a specific venue. 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. A demo for SBR is available at
  5. An automatically generated ontology of research topics in the Engineering field. To this end, we plan to create a new version of Klink, which is an algorithm that combines semantic technologies, machine learning, and knowledge from external sources to automatically generate a fully populated ontology of research areas.

In May 2019, as evidence for this successful collaboration, we renewed it with further objectives, including the creation variety of analytics solutions for analysing conferences, journals, books, organisations, topics and other research entities. In particular, we will focus on a Conference Dashboard, which will support editors in assessing the quality, impact, and trends of conferences. We will also focus on segmenting Springer Nature customers based on their interest (content they read) and support the Marketing department in tailoring their product packages.


More info

Springer Nature

Springer Nature is a leading research, educational and professional publisher, providing quality content to our communities through a range of innovative platforms, products and services. Every day, around the globe, our imprints, books, journals and resources reach millions of people – helping researchers, students, teachers and professionals to discover, learn and achieve more. Through our family of brands, we aim to serve and support the research, education and professional communities by putting them at the heart of all we do, delivering the highest possible standards in content and technology, and helping shape the future of publishing for their benefit and for society overall. Visit: and follow @SpringerNature.


Relevant papers

Springer Nature collaboration

Computer Science Ontology

CSO Classifier

Smart Topic Miner

Smart Book recommender

Klink algorithm

PhD work funded by Springer Nature