Biomedical Engineering Reference
In-Depth Information
Most human languages have evolved to require even the most basic
sentence to include a subject and a predicate (grammatically speaking, a
predicate is inclusive of the verb and object). To compose a sentence is to
assemble a snippet of meaning in a manner that can be readily interpreted
by the recipient. An RDF triple is the codifi cation of this requirement in
a format suitable for computation. The triple is, to a degree, a basic
sentence that can be intelligibly parsed by the computer. In short, RDF
provides a standard for assigning meaning to data and representing how
it interconnects, thus using semantics to defi ne meaning in computable
form. This simple but powerful design allows for computable forms of
not only entity information but also the associations between entities.
In addition to the advantages provided by the fl exibility of the RDF
format, technologies for use with RDF also offer some compelling
advantages. For example, RDF storage systems, commonly referred to as
'triple stores,' require little design. In comparison, the data models in
relational databases can comprise dozens of tables with complex
relationships between both the columns of a single table and between the
tables. Diagrams of these tables, their columns, and interconnections are
called a relational schema. The complexity of relational schemas can
mean that administrators intent on updating a relational data model may
need days or weeks to fully comprehend what will happen when values
are modifi ed in any given record. Furthermore, attempting to integrate
multiple schemas from multiple databases can take months of work. An
RDF triple store on the other hand comprises nothing more than a series
of triples. This means that the type and properties for all of the things
represented in the triple store are codifi ed in one format with a
standardized structure. As a result, integration across RDF triple stores,
or the inclusion of new triples in a triple store, is as easy as combining
sets of triples. Simply put, relational database schemas are fundamentally
a non-standardized data format because each schema is different. In
comparison, RDF is fundamentally a standardized data format thereby
enabling greater data integration fl exibility and interoperability.
RDF triple stores utilize a specialized query language called SPARQL
[14] that is similar to the query language for relational databases, SQL.
Despite the similarity between SPARQL and SQL, triple stores are easier
to query because their contents are not partitioned into tables as is usually
the case with relational data models. Furthermore, triple stores that are
version-compliant can be swapped in and out relatively easily. The same
cannot be said of relational database technologies where schemas usually
require careful modifi cation when being migrated or interconnected with
a different database.
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