Article,
OILing the way to machine understandable bioinformatics resources
IEEE Transactions on Information Technology in Biomedicine, (June 2002)
DOI: 10.1109/TITB.2002.1006300
Abstract
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
