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NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation

Overview of attention for article published in Journal of Biomedical Semantics, June 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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2 Facebook pages

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68 Dimensions

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100 Mendeley
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Title
NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation
Published in
Journal of Biomedical Semantics, June 2017
DOI 10.1186/s13326-017-0128-y
Pubmed ID
Authors

Marcos Martínez-Romero, Clement Jonquet, Martin J. O’Connor, John Graybeal, Alejandro Pazos, Mark A. Musen

Abstract

Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender , and via a Web service API).

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 100 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 22%
Researcher 15 15%
Student > Master 13 13%
Student > Bachelor 12 12%
Student > Doctoral Student 7 7%
Other 13 13%
Unknown 18 18%
Readers by discipline Count As %
Computer Science 42 42%
Biochemistry, Genetics and Molecular Biology 9 9%
Agricultural and Biological Sciences 8 8%
Engineering 4 4%
Nursing and Health Professions 2 2%
Other 13 13%
Unknown 22 22%
Attention Score in Context

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 24 July 2017.
All research outputs
#3,148,717
of 22,979,862 outputs
Outputs from Journal of Biomedical Semantics
#54
of 364 outputs
Outputs of similar age
#60,349
of 317,348 outputs
Outputs of similar age from Journal of Biomedical Semantics
#1
of 6 outputs
Altmetric has tracked 22,979,862 research outputs across all sources so far. Compared to these this one has done well and is in the 86th 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 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 317,348 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 80% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them