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Inferring ontology graph structures using OWL reasoning

Overview of attention for article published in BMC Bioinformatics, January 2018
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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Title
Inferring ontology graph structures using OWL reasoning
Published in
BMC Bioinformatics, January 2018
DOI 10.1186/s12859-017-1999-8
Pubmed ID
Authors

Miguel Ángel Rodríguez-García, Robert Hoehndorf

Abstract

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 16%
Student > Ph. D. Student 10 16%
Researcher 8 13%
Student > Bachelor 6 9%
Other 5 8%
Other 7 11%
Unknown 18 28%
Readers by discipline Count As %
Computer Science 23 36%
Agricultural and Biological Sciences 7 11%
Engineering 5 8%
Medicine and Dentistry 4 6%
Biochemistry, Genetics and Molecular Biology 3 5%
Other 5 8%
Unknown 17 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 08 February 2018.
All research outputs
#13,539,941
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#4,056
of 7,418 outputs
Outputs of similar age
#214,518
of 444,699 outputs
Outputs of similar age from BMC Bioinformatics
#60
of 140 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 44th percentile – i.e., 44% 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 444,699 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 140 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 57% of its contemporaries.