Biomedical Engineering Reference
In-Depth Information
Novel technologies have been developed over the course of the past
two decades that make it possible to address many of the downsides
of text-based search systems outlined above. These novel technologies
adopt a 'semantic' approach to the representation of entity properties
and their associations. With these technologies it is now possible to
design and build next-generation systems for scientifi c research, which
can handle large-volumes of data (Big Data), provide comprehensive
search results to a user query no matter what names might be used to
query for a given entity, and also automatically and dynamically
show the user all of the other entities known to be associated with their
queried entity.
19.3 Semantic technologies overview
The foundational set of 'semantic' technologies were originally proposed
in 2001, by Tim Berners-Lee and colleagues in a
Scientifi c American
article entitled, 'The Semantic Web' [12]. The idea of the Semantic Web
was to make information about entities and their associations with other
entities accessible for computation. A range of fl exible, open standard
semantic technologies have been developed over the last decade under the
leadership of the World Wide Web Consortium (W3C). These technologies
enable novel forms of facile integration of large quantities of disparate
data, representation of entities and entity associations in a computable
form. Combined with comprehensive querying and inferencing
capabilities (the ability to compute novel relationships 'on-the-fl y'),
semantic technologies promise collaborative knowledge building through
joint creation of computable data. Furthermore, the foundational
components of semantic technologies adhere to open, free standards that
make them an excellent framework on which to build next-generation
'Big Data' search and analysis systems.
Semantic technologies utilize a data formatting standard called resource
description framework or 'RDF' [13]. RDF defi nes a very simple but
highly fl exible data format called 'triples.' A triple is made up of a subject,
a predicate and an object. Here is an example:
subject
→
predicate
→
object
converted into simpler terms produces:
something
→
does something to
→
something else
or
something
→
has some
→
property
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