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Too many tags spoil the metadata: investigating the knowledge management of scientific research with semantic web technologies

Authors: Samantha Kanza; Nicholas Gibbins; Jeremy G. Frey;

Too many tags spoil the metadata: investigating the knowledge management of scientific research with semantic web technologies

Abstract

Scientific research is increasingly characterised by the volume of documents and data that it produces, from experimental plans and raw data to reports and papers. Researchers frequently struggle to manage and curate these materials, both individually and collectively. Previous studies of Electronic Lab Notebooks (ELNs) in academia and industry have identified semantic web technologies as a means for organising scientific documents to improve current workflows and knowledge management practices. In this paper, we present a qualitative, user-centred study of researcher requirements and practices, based on a series of discipline-specific focus groups. We developed a prototype semantic ELN to serve as a discussion aid for these focus groups, and to help us explore the technical readiness of a range of semantic web technologies. While these technologies showed potential, existing tools for semantic annotation were not well-received by our focus groups, and need to be refined before they can be used to enhance current researcher practices. In addition, the seemingly simple notion of "tagging and searching" documents appears anything but; the researchers in our focus groups had extremely personal requirements for how they organise their work, so the successful incorporation of semantic web technologies into their practices must permit a significant degree of customisation and personalisation.

Keywords

020, Semantic tagging, Information technology, T58.5-58.64, 004, Chemistry, Ontologies, Scientific documents, Document management, QD1-999, Research Article

  • BIP!
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    10
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Top 10%
Average
Average
Green
gold