A Common Layer of Interoperability for Biomedical Ontologies Based on OWL EL

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2011 Feb 24

PMID 21343142

Citations 23

Authors

Robert Hoehndorf

Michel Dumontier

Anika Oellrich

Sarala Wimalaratne

Dietrich Rebholz-Schuhmann

Paul Schofield

Georgios V Gkoutos

Affiliations

Soon will be listed here.

Abstract

Motivation: Ontologies are essential in biomedical research due to their ability to semantically integrate content from different scientific databases and resources. Their application improves capabilities for querying and mining biological knowledge. An increasing number of ontologies is being developed for this purpose, and considerable effort is invested into formally defining them in order to represent their semantics explicitly. However, current biomedical ontologies do not facilitate data integration and interoperability yet, since reasoning over these ontologies is very complex and cannot be performed efficiently or is even impossible. We propose the use of less expressive subsets of ontology representation languages to enable efficient reasoning and achieve the goal of genuine interoperability between ontologies.

Results: We present and evaluate EL Vira, a framework that transforms OWL ontologies into the OWL EL subset, thereby enabling the use of tractable reasoning. We illustrate which OWL constructs and inferences are kept and lost following the conversion and demonstrate the performance gain of reasoning indicated by the significant reduction of processing time. We applied EL Vira to the open biomedical ontologies and provide a repository of ontologies resulting from this conversion. EL Vira creates a common layer of ontological interoperability that, for the first time, enables the creation of software solutions that can employ biomedical ontologies to perform inferences and answer complex queries to support scientific analyses.

Availability And Implementation: The EL Vira software is available from http://el-vira.googlecode.com and converted OBO ontologies and their mappings are available from http://bioonto.gen.cam.ac.uk/el-ont.

Citing Articles

Literature-Based Enrichment Insights into Redox Control of Vascular Biology.

Essack M, Salhi A, Stanimirovic J, Tifratene F, Raies A, Hungler A Oxid Med Cell Longev. 2019; 2019:1769437.

PMID: 31223421 PMC: 6542245. DOI: 10.1155/2019/1769437.

Integrating phenotype ontologies with PhenomeNET.

Rodriguez-Garcia M, Gkoutos G, Schofield P, Hoehndorf R J Biomed Semantics. 2017; 8(1):58.

PMID: 29258588 PMC: 5735523. DOI: 10.1186/s13326-017-0167-4.

Neuro-symbolic representation learning on biological knowledge graphs.

Alshahrani M, Khan M, Maddouri O, Kinjo A, Queralt-Rosinach N, Hoehndorf R Bioinformatics. 2017; 33(17):2723-2730.

PMID: 28449114 PMC: 5860058. DOI: 10.1093/bioinformatics/btx275.

The anatomy of phenotype ontologies: principles, properties and applications.

Gkoutos G, Schofield P, Hoehndorf R Brief Bioinform. 2017; 19(5):1008-1021.

PMID: 28387809 PMC: 6169674. DOI: 10.1093/bib/bbx035.

Using AberOWL for fast and scalable reasoning over BioPortal ontologies.

Slater L, Gkoutos G, Schofield P, Hoehndorf R J Biomed Semantics. 2016; 7(1):49.

PMID: 27502585 PMC: 4976511. DOI: 10.1186/s13326-016-0090-0.

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