Re: FAQ:

On 20/5/09 21:57, Kevin Doyle wrote:
> Hi,
> I have a question I would like to put on the SKOS FAQ, because I don't
> know the answer. Also, this is the first place that I looked for the
> answer. Why SKOS and not OWL? Or maybe to put the question another way,
> what are the advantages of using SKOS over OWL?

Hi Kevin,

Did you see this section in the SKOS reference spec?

http://www.w3.org/TR/2009/CR-skos-reference-20090317/#L1045

Excerpted below. Does it help?

cheers,

Dan

[[
1.3. SKOS, RDF and OWL

The "elements" of the SKOS data model are classes and properties, and 
the structure and integrity of the data model is defined by the logical 
characteristics of, and interdependencies between, those classes and 
properties. This is perhaps one of the most powerful and yet potentially 
confusing aspects of SKOS, because SKOS can, in more advanced 
applications, also be used side-by-side with OWL to express and exchange 
knowledge about a domain. However, SKOS is not a formal knowledge 
representation language.

To understand this distinction, consider that the "knowledge" made 
explicit in a formal ontology is expressed as sets of axioms and facts. 
A thesaurus or classification scheme is of a completely different 
nature, and does not assert any axioms or facts. Rather, a thesaurus or 
classification scheme identifies and describes, through natural language 
and other informal means, a set of distinct "ideas" or "meanings", which 
are sometimes conveniently referred to as "concepts". These "concepts" 
may also be arranged and organized into various structures, most 
commonly hierarchies and association networks. These structures, 
however, do not have any formal semantics, and cannot be reliably 
interpreted as either formal axioms or facts about the world. Indeed 
they were never intended to be so, for they serve only to provide a 
convenient and intuitive "map" of some subject domain, which can then be 
used as an aid to organizing and finding objects, such as documents, 
which are relevant to that domain.

To make the "knowledge" embedded in a thesaurus or classification scheme 
explicit in any formal sense requires that the thesaurus or 
classification scheme be re-engineered as a formal ontology. In other 
words, some person has to do the work of transforming the structure and 
intellectual content of a thesaurus or classification scheme into a set 
of formal axioms and facts. This work of transformation is both 
intellectually demanding and time consuming, and therefore costly. Much 
can be gained from using thesauri etc. "as-is", as informal, convenient 
structures for navigation within a subject domain. Using them "as-is" 
does not require any re-engineering, and is therefore much less costly. 
In addition, some KOS are, by design, not intended to represent a 
logical view of their domain. Converting such KOS to a formal 
logic-based representation may, in practice, involve changes which 
result in a representation that no longer meets the originally intended 
purpose.

OWL does, however, provide a powerful data modeling language. We can, 
therefore, use OWL to construct a data model for representing thesauri 
or classification schemes "as-is". This is exactly what SKOS does. 
Taking this approach, the "concepts" of a thesaurus or classification 
scheme are modeled as individuals in the SKOS data model, and the 
informal descriptions about and links between those "concepts" as given 
by the thesaurus or classification scheme are modeled as facts about 
those individuals, never as class or property axioms. Note that these 
"facts" are facts about the thesaurus or classification scheme itself, 
such as "concept X has preferred label 'Y' and is part of thesaurus Z"; 
these are not facts about the way the world is arranged within a 
particular subject domain, as might be expressed in a formal ontology.

SKOS data are then expressed as RDF triples. For example, the RDF graph 
below (in [TURTLE] as discussed in Section 1.7.3) expresses some facts 
about a thesaurus.

<A> rdf:type skos:Concept ;
   skos:prefLabel "love"@en ;
   skos:altLabel "adoration"@en ;
   skos:broader <B> ;
   skos:inScheme <S> .

<B> rdf:type skos:Concept ;
   skos:prefLabel "emotion"@en ;
   skos:altLabel "feeling"@en ;
   skos:topConceptOf <S> .

<S> rdf:type skos:ConceptScheme ;
   dct:title "My First Thesaurus" ;
   skos:hasTopConcept <B> .

This point is vital to understanding the formal definition of the SKOS 
data model and how it may be implemented in software systems. This point 
is also vital to more advanced applications of SKOS, especially where 
SKOS and OWL are used in combination as part of a hybrid 
formal/semi-formal design.

 From a user's point of view, however, the distinction between a formal 
knowledge representation system and an informal or semi-formal knowledge 
organization system may naturally become blurred. In other words, it may 
not be relevant to a user that <A> and <B> in the graph below are 
individuals (instances of skos:Concept), and <C> and <D> are classes 
(instances of owl:Class) .

<A> rdf:type skos:Concept ;
   skos:prefLabel "love"@en ;
   skos:broader <B> .

<B> rdf:type skos:Concept ;
   skos:prefLabel "emotion"@en .

<C> rdf:type owl:Class ;
   rdfs:label "mammals"@en ;
   rdfs:subClassOf <D> .

<D> rdf:type owl:Class ;
   rdfs:label "animals"@en .

An information system that has any awareness of the SKOS data model 
will, however, need to appreciate the distinction.

RDF schemas for SKOS and the SKOS eXtension for Labels (XL) are 
described in Appendix C. SKOS Data Model as RDF Triples. Note that, as 
there are constraints that cannot be completely captured in the schema, 
the RDF/XML document provides a normative subset of this specification. ]]

Received on Wednesday, 20 May 2009 20:42:25 UTC