The American Journal of Botany has, in this month's issue, published an article by Ramona Walls (of New York Botanical Garden) and colleagues. The article describes how scientists build ontologies such as the Plant Ontology (PO) and how these tools can transform plant science by facilitating new ways of gathering and exploring data.
Ontologies are new tools that provide the rules computers need to transform information into knowledge, by attaching meaning to data, thereby making those data retrievable by computers and more understandable to human beings.
Ontology in this contemporary sense is a description of the types of entities within a given domain and the relationships among them. When data from many divergent sources, such as data about some specific plant organ, are associated or "tagged" with particular terms from a single ontology or set of interrelated ontologies, the data become easier to find. Computers can then use the logical relationships in the ontologies to correctly combine the information from the different databases. Moreover, computers can also use ontologies to aggregate data associated with the different subclasses or parts of entities.
The article describes four key areas of plant science that could benefit from the use of ontologies: (1) comparative genetics, genomics, phenomics, and development; (2) taxonomy and systematics; (3) semantic applications; and (4) education. Although most of the examples in this article are drawn from plant science, the principles could apply to any group of organisms, and the article should be of interest to zoologists as well.
As genomic and phenomic data become available for more species, many different research groups are embarking on the annotation of their data and images with ontology terms. At the same time, cross-species queries are becoming more common, causing more researchers in plant science to turn to ontologies. Ontology developers are working with the scientists who generate data to make sure ontologies accurately reflect current science, and with database developers and publishers to find ways to make it easier for scientist to associate their data with ontologies.
