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Developing a web-based SKOS editor

Overview of attention for article published in Journal of Biomedical Semantics, April 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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Title
Developing a web-based SKOS editor
Published in
Journal of Biomedical Semantics, April 2016
DOI 10.1186/s13326-015-0043-z
Pubmed ID
Authors

Mike Conway, Artem Khojoyan, Fariba Fana, William Scuba, Melissa Castine, Danielle Mowery, Wendy Chapman, Simon Jupp

Abstract

The Simple Knowledge Organization System (SKOS) was introduced to the wider research community by a 2005 World Wide Web Consortium (W3C) working draft, and further developed and refined in a 2009 W3C recommendation. Since then, SKOS has become the de facto standard for representing and sharing thesauri, lexicons, vocabularies, taxonomies, and classification schemes. In this paper, we describe the development of a web-based, free, open-source SKOS editor built for the development, curation, and management of small to medium-sized lexicons for health-related Natural Language Processing (NLP). The web-based SKOS editor allows users to create, curate, version, manage, and visualise SKOS resources. We tested the system against five widely-used, publicly-available SKOS vocabularies of various sizes and found that the editor is suitable for the development and management of small to medium-size lexicons. Qualitative testing has focussed on using the editor to develop lexical resources to drive NLP applications in two domains. First, developing a lexicon to support an Electronic Health Record-based NLP system for the automatic identification of pneumonia symptoms. Second, creating a taxonomy of lexical cues associated with Diagnostic and Statistical Manual of Mental Disorders (DSM-5) diagnoses with the goal of facilitating the automatic identification of symptoms associated with depression from short, informal texts. The SKOS editor we have developed is - to the best of our knowledge - the first free, open-source, web-based, SKOS editor capable of creating, curating, versioning, managing, and visualising SKOS lexicons.

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The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Finland 1 2%
Unknown 45 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 21%
Student > Ph. D. Student 8 17%
Researcher 5 11%
Student > Bachelor 4 9%
Student > Postgraduate 3 6%
Other 7 15%
Unknown 10 21%
Readers by discipline Count As %
Medicine and Dentistry 8 17%
Computer Science 8 17%
Engineering 4 9%
Agricultural and Biological Sciences 3 6%
Social Sciences 3 6%
Other 8 17%
Unknown 13 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 September 2017.
All research outputs
#4,161,768
of 22,860,626 outputs
Outputs from Journal of Biomedical Semantics
#66
of 364 outputs
Outputs of similar age
#65,643
of 300,360 outputs
Outputs of similar age from Journal of Biomedical Semantics
#2
of 14 outputs
Altmetric has tracked 22,860,626 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 300,360 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.