Database Reference
In-Depth Information
or ontologies, as described in the following subsection) so that they can be
consistent. Another example of scientific annotations is in the neuroscience
domain, where scientists are able to annotate a number of brain images.
7
The
issue in brain imaging is that there are very few automated techniques that
can extract the features in an image. Rather, the analysis of the image is
often done by a scientist. In some cases, the images need to be classified or
annotated based on the functional properties of the brain, information that
cannot be automatically extracted. As with other annotations, brain images
can be annotated by various individuals, using different terms from an overall
vocabulary. An advantage of using a controlled vocabulary for the annotations
is that the annotations can be queried and thus data can be discovered based
on these annotations.
Annotations can also be used in the context of scientific workflows (see
Chapter 13), where workflow components or entire workflows can be anno-
tated so that they can be more readily discovered and evaluated for suitability.
The myGrid project
8
has a particular emphasis on bioinformatics workflows
composed of services broadly available to the community. These services are
annotated with information about their functionality and characteristics. my-
Grid annotations can be both in a free text form and drawn from a controlled
vocabulary.
9
12.2.1 The Role of Ontologies in Metadata Specification
Together with controlled vocabularies and thesauri, ontologies have become
one of the most common means to specify the structure of metadata in scien-
tific applications, such as the previous ones. Ontologies are normally defined
as “formal, explicit specifications of shared conceptualizations”.
10
A
concep-
tualization
is an abstract model of some phenomenon in the world derived by
having identified the relevant concepts of that phenomenon.
Explicit
means
that the type of concepts used, and the constraints on their use, are explicitly
defined.
Formal
refers to the fact that the ontology should be machine read-
able.
Shared
reflects the notion that an ontology captures consensual knowl-
edge; that is, it is not private view for some individual, but accepted by
a group.
Ontologies started to be used for this purpose in document metadata anno-
tation approaches in pre-Semantic Web applications like the SHOE project,
11
the (KA)
2
initiative),
12
and PlanetOnto,
13
among others. Later ontologies have
become a commodity for specifying the schema of metadata annotations, not
only about Web documents, but also about all types of Web and non-Web
resources. The benefits they provide with respect to other artifacts are mainly
related to the fact that they capture consensual knowledge (what facilitates
interoperability between applications, although consensus is sometimes di-
cult to achieve, as described in the previous section), and that they can be
used to check the consistency of the annotations and to infer new knowledge
