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Ontorat: automatic generation of new ontology terms, annotations, and axioms based on ontology design patterns.

Overview of attention for article published in Journal of Biomedical Semantics, January 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#27 of 197)
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

news
1 news outlet
twitter
2 tweeters

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
26 Mendeley
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Title
Ontorat: automatic generation of new ontology terms, annotations, and axioms based on ontology design patterns.
Published in
Journal of Biomedical Semantics, January 2015
DOI 10.1186/2041-1480-6-4
Pubmed ID
Authors

Xiang Z, Zheng J, Lin Y, He Y

Abstract

It is time-consuming to build an ontology with many terms and axioms. Thus it is desired to automate the process of ontology development. Ontology Design Patterns (ODPs) provide a reusable solution to solve a recurrent modeling problem in the context of ontology engineering. Because ontology terms often follow specific ODPs, the Ontology for Biomedical Investigations (OBI) developers proposed a Quick Term Templates (QTTs) process targeted at generating new ontology classes following the same pattern, using term templates in a spreadsheet format.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 4%
Unknown 25 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 23%
Student > Ph. D. Student 6 23%
Student > Bachelor 4 15%
Professor > Associate Professor 2 8%
Student > Postgraduate 2 8%
Other 3 12%
Unknown 3 12%
Readers by discipline Count As %
Computer Science 10 38%
Agricultural and Biological Sciences 8 31%
Engineering 2 8%
Biochemistry, Genetics and Molecular Biology 1 4%
Psychology 1 4%
Other 2 8%
Unknown 2 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 20 January 2015.
All research outputs
#459,702
of 4,892,609 outputs
Outputs from Journal of Biomedical Semantics
#27
of 197 outputs
Outputs of similar age
#21,880
of 172,349 outputs
Outputs of similar age from Journal of Biomedical Semantics
#4
of 12 outputs
Altmetric has tracked 4,892,609 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 197 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 85% 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 172,349 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 86% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.