This article is part of the supplement: Proceedings of the 10th Bio-Ontologies Special Interest Group Workshop 2007. Ten years past and looking to the future
Ontology Design Patterns for bio-ontologies: a case study on the Cell Cycle Ontology
1 School of Computer Science, University of Manchester, Oxford Road, M13 9PL Manchester, UK
2 Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium
3 Department of Molecular Genetics, Ghent University, Technologiepark 927, 9052 Gent, Belgium
BMC Bioinformatics 2008, 9(Suppl 5):S1 doi:10.1186/1471-2105-9-S5-S1Published: 29 April 2008
Bio-ontologies are key elements of knowledge management in bioinformatics. Rich and rigorous bio-ontologies should represent biological knowledge with high fidelity and robustness. The richness in bio-ontologies is a prior condition for diverse and efficient reasoning, and hence querying and hypothesis validation. Rigour allows a more consistent maintenance. Modelling such bio-ontologies is, however, a difficult task for bio-ontologists, because the necessary richness and rigour is difficult to achieve without extensive training.
Analogous to design patterns in software engineering, Ontology Design Patterns are solutions to typical modelling problems that bio-ontologists can use when building bio-ontologies. They offer a means of creating rich and rigorous bio-ontologies with reduced effort. The concept of Ontology Design Patterns is described and documentation and application methodologies for Ontology Design Patterns are presented. Some real-world use cases of Ontology Design Patterns are provided and tested in the Cell Cycle Ontology. Ontology Design Patterns, including those tested in the Cell Cycle Ontology, can be explored in the Ontology Design Patterns public catalogue that has been created based on the documentation system presented (http://odps.sourceforge.net/ webcite).
Ontology Design Patterns provide a method for rich and rigorous modelling in bio-ontologies. They also offer advantages at different development levels (such as design, implementation and communication) enabling, if used, a more modular, well-founded and richer representation of the biological knowledge. This representation will produce a more efficient knowledge management in the long term.