On Fri, 09 Jan 2009 12:54:08 +1100, Calogero Alex Baldacchino
<alex.baldacch...@email.it> wrote:
I admit I'm not very expert in RDF use, thus I have a few questions.
Specifically, maybe I can guess the advantages when using the same
(carefully modelled, and well-known) vocabulary/ies; but when two
organizations develop their own vocabularies, similar yet different, to
model the same kind of informations, is merging of data enough? Can a
processor give more than a collection of triples, to be then interpreted
basing on knowledge on the used vocabulary/ies?
RDF consists of several parts. One of the key parts explains how to make
an RDF vocabulary self-describing in terms of other vocabularies.
I mean, I assume my tools can extract RDF(a) data from whatever
document, but my query interface is based on my own vocabulary: when I
merge informations from an external vocabulary, do I need to translate
one vocabulary to the other (or at least to modify the query backend, so
that certain curies are recognized as representing the same concepts -
e.g. to tell my software that 'foaf:name' and 'ex:someone' are
equivalent, for my purposes)? If so, merging data might be the minor
part of the work I need to do, with respect to non-RDF(a) metadata (that
is, I'd have tools to extract and merge data anyway, and once I
translated external metadata to my format, I could use my own tools to
merge data), specially if the same model is used both by mine and an
external organization (therefore requiring an easier translation).
If a vocabulary is described, then you can do an automated translation
from one RDF vocabulary to another by using your original query based in
your original vocabulary. This is one of the strengths of RDF.
Thus, I'm thinking the most valuable benefit of using RDF/RDFa is the
sureness that both parties are using the very same data model, despite
the possible use of different vocabularies -- it seems to me that the
concept of triples consisting of a subject, a predicate and an object is
somehow similar to a many-to-many association in a database, whereas one
might prefer a one-to-many approach - though, the former might be a
natural choice to model data which are usually sparse, as in a document
prose.
I don't see the ananlogy, but yes, I think the big benefit is being able
to ensure that you know the data model without knowing the vocabulary a
priori - since this is sufficient to automate the process of merging data
into your model.
cheers
Chaals
--
Charles McCathieNevile Opera Software, Standards Group
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