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Journal Article

Improving ontologies by automatic reasoning and evaluation of logical definitions

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Robinson,  P. N.
Research Group Development & Disease (Head: Stefan Mundlos), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Kohler, S., Bauer, S., Mungall, C. J., Carletti, G., Smith, C. L., Schofield, P., et al. (2011). Improving ontologies by automatic reasoning and evaluation of logical definitions. BMC Bioinformatics, 12, 418. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/22032770 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224779/pdf/1471-2105-12-418.pdf?tool=pmcentrez.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-7820-6
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.