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Artificial Intelligence-Led Content Publishing, Metadata Creation, and Knowledge Discovery: In Quest of Sustainable and Profitable Business Models

Artificial Intelligence-Led Content Publishing, Metadata Creation, and Knowledge Discovery: In Quest of Sustainable and Profitable Business Models

Usha B. Biradar (Molecular Connections Pvt Ltd., Bangalore, India), Lokanath Khamari (Molecular Connections Pvt Ltd., India), and Jignesh Bhate (Molecular Connections Pvt Ltd., India)
Copyright: © 2021 |Pages: 16
DOI: 10.4018/978-1-7998-5589-7.ch010
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Abstract

Digital transitions have had strong headwinds in scholarly publishing for the past decade. It started with digitising content and is resting somewhere between tying up diverse content and catering to diverse end users. The goal is still to keep up with the changing landscape, and a demonstrable way of doing so is to actively participate by quickly adapting to standards. Artificial intelligence (AI) has a proven track record of helping with this and is an integral part of the solution frameworks. The chapter content includes a brief insight into some practices and workflows within scholarly publishing that stand to benefit from direct intervention of AI. These include editorial decision systems, metadata enrichments, metadata standardization, and search augmentations. The authors bring to light various developments in scholarly publishing and the status of some of the best implementations of AI techniques in aiding and upkeep of the ‘digital transformations'.
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Introduction

Digital transitions have had strong headwinds in Scholarly Publishing for the past decade. It started with digitising content and is resting somewhere between tying up diverse contents and catering to diverse end users. Goal is still to keep up with the changing landscape and a demonstrable way of doing so is to actively participate by quickly adapting to standards. Artificial Intelligence has a proven track record of helping with this and is an integral part of the solution frameworks.

This chapter is aimed at providing an insight into the state-of-the-art Artificial Intelligence applications in scholarly publishing and how these can be leveraged to bring to speed various participants within the ecosystem and as an added benefit, target new engagements and revenue streams. Individual solutions are presented here to demonstrate how Artificial Intelligence can be exploited to automate, gain insights, pave opportunities and streamline existing pipelines across various stages of scholarly publishing

Chapter Content

Authors enumerate some practices and workflows within scholarly publishing that stand to benefit from direct intervention of Artificial Intelligence, namely:

  • 1.

    Editorial Decision Systems

  • 2.

    Metadata Enrichments

  • 3.

    Search Augmentations

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Editorial Decision Systems

Introduction

Editors play a vital role in the entire process of scholarly publication life cycle. These tasks include but are not limited to literal reviews, directing and distributing incoming content to respective domain experts/peer reviewers, looking out for traffic rates across domains/ subjects of expertise that a particular publication is offering, acceptance and rejection rates across various publications, delays at checkpoints across the workflow and general smooth sailing of the entire process (Oryila & Aghadiuno, 2019).

Responsibilities of certain tasks also include ensuring ethical means are followed along with obvious technical rules and regulations set by the publication and meeting the standards set out by the scholarly community as a whole (Da Silva & Dobránszki 2017). With regards to this any and all help in the form of automating and providing insights is a necessity. Especially so with the volume and expansion in data and content that the world is witnessing currently.

Some of the answers to the questions that editors are in quest for are:

Automations

How can automation be employed to direct incoming traffic to peer reviewers? What prerequisites might one need to employ these automations successfully? How to measure the efficiency and effectiveness of these solutions?

Content Validity and Currentness

What are the densities of published content with respect to topic/subjects that a publication offers. Is there an apparent trend when compared to archives vs recent content? Does this point to any obsolete topics/subjects that are seeing little or no traffic in terms of new publications? How can the adherence of journal scopes to the actual content published be confirmed and validated?

Landscaping

How to identify content overlap amongst publications and remedy/redirect them if necessary? Identify traffic in special issues with respect to topics/subjects vs generic traffic

Inclusivity

How can actionable insights be derived from any feedback forums that are enabled either directly or indirectly in the workflow? Directly in terms of author inputs during submissions and reviews, custom questionnaires and surveys etc., and indirectly via social media.

Opportunity

How can the current and upcoming content needs be recognised and fulfilled? Can the turnaround times be tailored to need of the hour without disrupting the current workflow? How to identify and fulfill content repurposing profitably and efficiently?

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