↓ Skip to main content

FEDRR: fast, exhaustive detection of redundant hierarchical relations for quality improvement of large biomedical ontologies

Overview of attention for article published in BioData Mining, October 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
12 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
FEDRR: fast, exhaustive detection of redundant hierarchical relations for quality improvement of large biomedical ontologies
Published in
BioData Mining, October 2016
DOI 10.1186/s13040-016-0110-8
Pubmed ID
Authors

Guangming Xing, Guo-Qiang Zhang, Licong Cui

Abstract

Redundant hierarchical relations refer to such patterns as two paths from one concept to another, one with length one (direct) and the other with length greater than one (indirect). Each redundant relation represents a possibly unintended defect that needs to be corrected in the ontology quality assurance process. Detecting and eliminating redundant relations would help improve the results of all methods relying on the relevant ontological systems as knowledge source, such as the computation of semantic distance between concepts and for ontology matching and alignment. This paper introduces a novel and scalable approach, called FEDRR - Fast, Exhaustive Detection of Redundant Relations - for quality assurance work during ontological evolution. FEDRR combines the algorithm ideas of Dynamic Programming with Topological Sort, for exhaustive mining of all redundant hierarchical relations in ontological hierarchies, in O(c·|V|+|E|) time, where |V| is the number of concepts, |E| is the number of the relations, and c is a constant in practice. Using FEDRR, we performed exhaustive search of all redundant is-a relations in two of the largest ontological systems in biomedicine: SNOMED CT and Gene Ontology (GO). 372 and 1609 redundant is-a relations were found in the 2015-09-01 version of SNOMED CT and 2015-05-01 version of GO, respectively. We have also performed FEDRR on over 190 source vocabularies in the UMLS - a large integrated repository of biomedical ontologies, and identified six sources containing redundant is-a relations. Randomly generated ontologies have also been used to further validate the efficiency of FEDRR. FEDRR provides a generally applicable, effective tool for systematic detecting redundant relations in large ontological systems for quality improvement.

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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 42%
Student > Postgraduate 2 17%
Student > Doctoral Student 2 17%
Researcher 2 17%
Unknown 1 8%
Readers by discipline Count As %
Computer Science 6 50%
Medicine and Dentistry 2 17%
Biochemistry, Genetics and Molecular Biology 1 8%
Agricultural and Biological Sciences 1 8%
Unspecified 1 8%
Other 0 0%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 14 October 2016.
All research outputs
#6,155,023
of 8,515,357 outputs
Outputs from BioData Mining
#166
of 195 outputs
Outputs of similar age
#163,255
of 253,463 outputs
Outputs of similar age from BioData Mining
#9
of 10 outputs
Altmetric has tracked 8,515,357 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 195 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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 253,463 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one.