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Submit a Manuscript to the Journal
Journal of Library Metadata

For a Special Issue on
Emerging Advances in Collective Intelligence for Collaborative and Participatory Information and Metadata Management in Digital Library Systems

Abstract deadline
20 May 2022

Manuscript deadline
30 July 2022

Cover image - Journal of Library Metadata

Special Issue Editor(s)

Dr. BalaAnand Muthu, Associate Professor, Department of Computer Science & Engineering, Adhiyamaan College of Engineering, India
[email protected]

Dr. Imran Shafique Ansari, James Watt School of Engineering University of Glasgow, UK
[email protected]

Dr. Xuan Liu (yusuf), College of Information Engineering (College of Artificial Intelligence), Yangzhou University, China
[email protected]

Submit an ArticleVisit JournalArticles

Emerging Advances in Collective Intelligence for Collaborative and Participatory Information and Metadata Management in Digital Library Systems

Disruptive technologies are revolutionizing almost every field. The popularization of the Internet causes a continuous increase in the growing number of digital library systems that provide electronic access to various kinds of documents. These libraries link together data from multiple sources, as well as from local databases. Users need special navigational tools that allow them to find information on specific topics in a short time and with ease. Even though there are improvements of automatic indexing methods, standard solutions are not adequate for digital libraries. This is because they only represent quantitative information such as word frequency or its part of speech, giving little hints about the content of documents in a collection. Information retrieval systems can be improved by exploiting the knowledge related to document description, i.e. the metadata that describes their basic elements and actual content - typically used tags which refer to topic categories and labels that reveal specifics of a given domain or an area studied by experts. Metadata is a crucial element in digital library systems, due to the immense amount of information the user faces. Effective metadata management is the key to providing data with meaning and structure, thereby helping users to find what they are looking for. Collective intelligence offers a great variety of methods, which can be used as a flexible toolkit to enhance the quality of metadata management in digital libraries.

Through collective intelligence and the inclusion of social networks in digital library management systems, it becomes easy to find, preserve, generate, share, and explore content. Through collective crowdsourcing, resources can be found as easily as finding a video on social media. Methods that are based on collective intelligence for collaborative and participatory information management offer time and cost-effective solutions. There are a lot of methods and tools that enable sharing of knowledge on informal communication platforms. Knowledge trees, recommendations, and voice-view systems are some techniques that are effective in managing scalable, dynamic, and flexible information management interfaces. Thus, this special issue investigates the significance of metadata in digital library systems and the role of collective intelligence in enhancing their design and management. To this end, we also invite researchers and professionals to submit their innovative solutions for the betterment of this field. This special issue investigates the significance of metadata in digital library systems and the role of collective intelligence in enhancing their design and management.

Topics: Recommended topics include, but are not limited to the following:

  • Tools to filter and track information uploaded by users in a distributed and collective intelligence system for metadata and information management in the digital library
  • Integration of Artificial Intelligence (AI) and Big Data analytics for collaborative and participatory information management in digital library systems
  • Modern metadata management in digital libraries with collective intelligence and collaborative participatory systems
  • Novel techniques and tools for promoting personalized learning and metadata management systems in digital library interfaces
  • Advances in metadata and semantic research with collective intelligence for digital libraries
  • Metadata reusability with collective intelligence for digital libraries
  • Collective intelligence for metadata and knowledge management in digital libraries
  • Advances in metadata and ontology for digital libraries
  • User-driven social networks for collective intelligence in the digital library ecosystem
  • The design of an interactive and hybrid model of a recommender system for the digital library
  • Improving the trustworthiness of information shared or posted using collective intelligence tools
  • Data Science and analytics for measuring the trends in content search for improving deep learning systems in a collective intelligence framework
  • The development of a sustainable and energy-efficient digital library system with collective intelligence.

Submission Instructions

Submission Procedure: Researchers and practitioners are invited to submit on or before May 20, 2022, a proposal (between 500 to 700 words) clearly explaining the objectives and concerns of his or her proposed article. Authors of accepted proposals will be notified shortly about the status of their proposals. Full manuscripts (3000-7000 words) are expected to be submitted by July 30, 2022. All submitted manuscripts will be reviewed on a double-blind review basis.
Please forward proposal submissions electronically (Word document) to the guest editors at:

Dr. BalaAnand Muthu: [email protected]
Dr. Imran Shafique Ansari: [email protected]
Dr. Xuan Liu: [email protected]

Instructions for AuthorsSubmit an Article

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