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Matching disease and phenotype ontologies in the ontology alignment evaluation initiative

Overview of attention for article published in Journal of Biomedical Semantics, December 2017
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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 (79th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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7 tweeters
patent
2 patents

Citations

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15 Dimensions

Readers on

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41 Mendeley
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2 CiteULike
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Title
Matching disease and phenotype ontologies in the ontology alignment evaluation initiative
Published in
Journal of Biomedical Semantics, December 2017
DOI 10.1186/s13326-017-0162-9
Pubmed ID
Authors

Ian Harrow, Ernesto Jiménez-Ruiz, Andrea Splendiani, Martin Romacker, Peter Woollard, Scott Markel, Yasmin Alam-Faruque, Martin Koch, James Malone, Arild Waaler

Abstract

The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease. Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogMap(Bio) and PhenoMF systems produced the top results for ontology matching in comparison to consensus alignments. The results against manually curated mappings proved to be more difficult most likely because these mapping sets comprised mostly subsumption relationships rather than equivalence. Manual assessment of unique equivalence mappings showed that AML, LogMap(Bio) and PhenoMF systems have the highest precision results. Four systems gave the highest performance for matching disease and phenotype ontologies. These systems coped well with the detection of equivalence matches, but struggled to detect semantic similarity. This deserves more attention in the future development of ontology matching systems. The findings of this evaluation show that such systems could help to automate equivalence matching in the workflow of curators, who maintain ontology mapping services in numerous domains such as disease and phenotype.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 20%
Student > Ph. D. Student 7 17%
Student > Master 6 15%
Other 4 10%
Lecturer 4 10%
Other 9 22%
Unknown 3 7%
Readers by discipline Count As %
Computer Science 13 32%
Agricultural and Biological Sciences 7 17%
Biochemistry, Genetics and Molecular Biology 6 15%
Engineering 4 10%
Medicine and Dentistry 3 7%
Other 5 12%
Unknown 3 7%

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 07 September 2021.
All research outputs
#3,120,591
of 19,026,578 outputs
Outputs from Journal of Biomedical Semantics
#58
of 340 outputs
Outputs of similar age
#86,161
of 427,817 outputs
Outputs of similar age from Journal of Biomedical Semantics
#7
of 37 outputs
Altmetric has tracked 19,026,578 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 340 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 82% 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 427,817 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 79% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.