Improving ontologies by automatic reasoning and evaluation of logical definitions

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.