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Improving ontologies by automatic reasoning and evaluation of logical definitions

Sebastian Köhler12*, Sebastian Bauer1, Chris J Mungall3, Gabriele Carletti4, Cynthia L Smith5, Paul Schofield56, Georgios V Gkoutos7 and Peter N Robinson128*

Author affiliations

1 Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany

2 Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany

3 Lawrence Berkeley National Laboratory, Mail Stop 64R0121, Berkeley, CA 94720, USA

4 Dipartimento di Matematica e Informatica, Università di Camerino, Via Madonna delle Carceri 9, 62032 Camerino (MC), Italy

5 The Jackson Laboratory, Bar Harbor, ME 04609, USA

6 Department of Physiology, Development, and Neuroscience, University of Cambridge, Cambridge, CB2 3EG, UK

7 Department of Genetics, University of Cambridge, Downing Street, Cambridge, Cambridge CB2 3EH, UK

8 Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany

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Citation and License

BMC Bioinformatics 2011, 12:418  doi:10.1186/1471-2105-12-418

Published: 27 October 2011

Abstract

Background

Ontologies are widely used to represent knowledge in biomedicine. Systematic approaches for detecting errors and disagreements are needed for large ontologies with hundreds or thousands of terms and semantic relationships. A recent approach of defining terms using logical definitions is now increasingly being adopted as a method for quality control as well as for facilitating interoperability and data integration.

Results

We show how automated reasoning over logical definitions of ontology terms can be used to improve ontology structure. We provide the Java software package GULO (Getting an Understanding of LOgical definitions), which allows fast and easy evaluation for any kind of logically decomposed ontology by generating a composite OWL ontology from appropriate subsets of the referenced ontologies and comparing the inferred relationships with the relationships asserted in the target ontology. As a case study we show how to use GULO to evaluate the logical definitions that have been developed for the Mammalian Phenotype Ontology (MPO).

Conclusions

Logical definitions of terms from biomedical ontologies represent an important resource for error and disagreement detection. GULO gives ontology curators a fast and simple tool for validation of their work.