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dc.contributor.authorRodriguez-Garcia, Miguel Angel
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2018-01-28T07:01:37Z
dc.date.available2018-01-28T07:01:37Z
dc.date.issued2018-01-05
dc.identifier.citationRodríguez-García MÁ, Hoehndorf R (2018) Inferring ontology graph structures using OWL reasoning. BMC Bioinformatics 19. Available: http://dx.doi.org/10.1186/s12859-017-1999-8.
dc.identifier.issn1471-2105
dc.identifier.pmid29304741
dc.identifier.doi10.1186/s12859-017-1999-8
dc.identifier.urihttp://hdl.handle.net/10754/626867
dc.description.abstractOntologies 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.
dc.description.sponsorshipThis work has been supported by funding from King Abdullah University of Science and Technology (KAUST).
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1186/s12859-017-1999-8
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectOntology
dc.subjectOwl
dc.subjectAutomated Reasoning
dc.subjectSemantic Similarity
dc.subjectOntology Visualization
dc.titleInferring ontology graph structures using OWL reasoning
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBMC Bioinformatics
dc.eprint.versionPublisher's Version/PDF
kaust.personRodriguez-Garcia, Miguel Angel
kaust.personHoehndorf, Robert
dc.relation.issupplementedbyDOI:10.6084/m9.figshare.c.3970353.v1
dc.relation.issupplementedbyDOI:10.6084/m9.figshare.5765055.v1
dc.relation.issupplementedbyDOI:10.6084/m9.figshare.5765031.v1
refterms.dateFOA2018-06-14T03:37:57Z
display.relations<b> Is Supplemented By:</b> <br/> <ul> <li><i>[Dataset]</i> <br/> . DOI: <a href="https://doi.org/10.6084/m9.figshare.c.3970353.v1">10.6084/m9.figshare.c.3970353.v1</a> HANDLE: <a href="http://hdl.handle.net/10754/663989">10754/663989</a></li></ul><b> Is Supplemented By:</b> <br/> <ul> <li><i>[Dataset]</i> <br/> . DOI: <a href="https://doi.org/10.6084/m9.figshare.5765055.v1">10.6084/m9.figshare.5765055.v1</a> HANDLE: <a href="http://hdl.handle.net/10754/663990">10754/663990</a></li></ul><b> Is Supplemented By:</b> <br/> <ul> <li><i>[Dataset]</i> <br/> . DOI: <a href="https://doi.org/10.6084/m9.figshare.5765031.v1">10.6084/m9.figshare.5765031.v1</a> HANDLE: <a href="http://hdl.handle.net/10754/663993">10754/663993</a></li></ul>
dc.date.published-online2018-01-05
dc.date.published-print2018-12


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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.