Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning

Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of kn...

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Bibliographic Details
Published in:PloS one 2011-07, Vol.6 (7), p.e22006-e22006
Main Authors: Hoehndorf, Robert, Dumontier, Michel, Oellrich, Anika, Rebholz-Schuhmann, Dietrich, Schofield, Paul N, Gkoutos, Georgios V
Format: Article
Language:English
Subjects:
Apoptosis
Artificial intelligence
Automated reasoning
Automation
Bioinformatics
Biology
Biomedical Research
Computer Science
Data integration
Data mining
Genomes
Genotype & phenotype
Informatics
Information Storage and Retrieval
Information systems
Integration
International conferences
Interoperability
Knowledge
Knowledge base
Knowledge discovery
Knowledge representation
Large scale integration
Life sciences
Medicine
Molecular biology
Natural Language Processing
Ontology
Reasoning programs
Semantic web
Semantics
Social and Behavioral Sciences
Taxonomy
Vocabularies & taxonomies
Web Ontology Language-OWL
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Description
Summary:Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0022006