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Improving Editorial Workflow and Metadata Quality at Springer Nature

Salatino, Angelo; Osborne, Francesco; Birukou, Aliaksandr and Motta, Enrico (2019). Improving Editorial Workflow and Metadata Quality at Springer Nature. In: The 18th International Semantic Web Conference (ISWC 2019), 26-30 Oct 2019, Auckland, New Zealand, (In Press).

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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.

Item Type: Conference or Workshop Item
Keywords: Scholarly Data; Bibliographic Metadata; Topic Classification; Topic Detection; Scholarly Ontologies; Data Mining
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 62265
Depositing User: Angelo Salatino
Date Deposited: 04 Jul 2019 08:20
Last Modified: 13 Nov 2019 08:39
URI: http://oro.open.ac.uk/id/eprint/62265
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These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

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