Improving Editorial Workflow and Metadata Quality at Springer Nature

Abstract

Identifying the research topics that best describe the scope of a scientific publication is a crucial task for editors, in particular because the quality of these annotations determine how effectively users are able to discover the right content in online libraries. For this reason, Springer Nature, the world's largest academic book publisher, has traditionally entrusted this task to their most expert editors. These editors manually analyse all new books, possibly including hundreds of chapters, and produce a list of the most relevant topics. Hence, this process has traditionally been very expensive, time-consuming, and confined to a few senior editors. For these reasons, back in 2016 we developed Smart Topic Miner (STM), an ontology-driven application that assists the Springer Nature editorial team in annotating the volumes of all books covering conference proceedings in Computer Science. Since then STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year. Over the past three years the initial prototype has iteratively evolved in response to feedback from the users and evolving requirements. In this paper we present the most recent version of the tool and describe the evolution of the system over the years, the key lessons learnt, and the impact on the Springer Nature workflow. In particular, our solution has drastically reduced the time needed to annotate proceedings and significantly improved their discoverability, resulting in 9.3 million additional downloads. We also present a user study involving 9 editors, which yielded excellent results in term of usability, and report an evaluation of the new topic classifier used by STM, which outperforms previous versions in recall and F-measure.

The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

“…The main result of this collaboration are two applications which demonstrate the value of using CSO in the context of developing intelligent functionalities that take as input scholarly entities: Smart Topics Miner and Smart Book Recommender. [3,40] (STM)  21 is a tool developed for supporting the Springer Nature editorial team in classifying editorial products according to a taxonomy of research topics drawn both from CSO and the Product Market Codes (PMC), Springer Nature's own editorial classification system. This information is then used for: i) classifying proceedings in digital and physical libraries; ii) enhancing semantically the metadata associated with publications and consequently improving the discoverability of the proceedings; and iii) detecting promising emerging research areas that may deserve more attention from the publisher.…”

ResearchFlow: Understanding the Knowledge Flow Between Academia and Industry

“…We annotated publications and patents using the CSO Classifier 16 [33], an open-source Python tool for annotating documents with research topics from CSO. This is the same classifier that powers the Smart Topic Miner [34], which is the application used by Springer Nature for annotating Proceedings Book in Computer Science. The resulting set of topics was enriched by including all their super-topics in CSO.…”

AI-KG: An Automatically Generated Knowledge Graph of Artificial Intelligence

“…An alternative vision, that is gaining traction in the last few years, is to generate a semantically rich and interlinked description of the content of research publications [13,29,7,24]. Integrating this data would ultimately allow us to produce large scale knowledge graphs describing the state of the art in a field and all the relevant entities, e.g., tasks, methods, metrics, materials, experiments, and so on.…”

“…The research community has been working for several years on different solutions to enable a machine-readable representations of research, e.g., by creating bibliographic repositories in the Linked Data Cloud [19], generating knowledge bases of biological data [5], encouraging the Semantic Publishing paradigm [27], formalising research workflows [31], implementing systems for managing nanopublications [14] and micropublications [26], , automatically annotating research publications [24], developing a variety of ontologies to describe scholarly data, e.g., SWRC 6 , BIBO 7 , BiDO 8 , SPAR [21], CSO 9 [25], and generating large-scale knowledge graphs, e.g., OpenCitation 10 , Open Academic Graph 11 , Open Research Knowledge Graph 12 [13], Academia/Industry DynAmics (AIDA) Knowledge Graph 13 [3]. Most knowledge graphs in the scholarly domain typically contain metadata describing entities, such as authors, venues, organizations, research topics, and citations.…”