OILing the way to machine understandable bioinformatics resources

The complex questions and analyses posed by biologists, as well as the diverse data resources they develop, require the fusion of evidence from different, independently developed, and heterogeneous resources. The web, as an enabler for interoperability, has been an excellent mechanism for data publication and transportation. Successful exchange and integration of information, however, depends on a shared language for communication (a terminology) and a shared understanding of what the data means (an ontology). Without this kind of understanding, semantic heterogeneity remains a problem for both humans and machines. One means of dealing with heterogeneity in bioinformatics resources is through terminology founded upon an ontology. Bioinformatics resources tend to be rich in human readable and understandable annotation, with each resource using its own terminology. These resources are machine readable, but not machine understandable. Ontologies have a role in increasing this machine understanding, reducing the semantic heterogeneity between resources and thus promoting the flexible and reliable interoperation of bioinformatics resources. This paper describes a solution derived from the semantic web [a machine understandable world-wide web (WWW)], the ontology inference layer (OIL), as a solution for semantic bioinformatics resources. The nature of the heterogeneity problems are presented along with a description of how metadata from domain ontologies can be used to alleviate this problem. A companion paper in this issue gives an example of the development of a bio-ontology using OIL.