SKOS Simple Knowledge Organization System Reference

SKOS Simple Knowledge Organization System Reference
SKOS Simple Knowledge Organization System
Reference
W3C Recommendation 18 August 2009
This version:
Latest version:
Previous versions:
Editors:
Alistair
Miles
, STFC
Rutherford Appleton Laboratory / University of Oxford
Sean
Bechhofer
University of Manchester
Please refer to the
errata
for this
document, which may include some normative corrections.
See also
translations
W3C
MIT
ERCIM
Keio
), All Rights Reserved. W3C
liability
trademark
and
document
use
rules apply.
Abstract
This document defines the Simple Knowledge Organization System (SKOS), a
common data model for sharing and linking knowledge organization systems via
the Web.
Many knowledge organization systems, such as thesauri, taxonomies,
classification schemes and subject heading systems, share a similar
structure, and are used in similar applications. SKOS captures much of this
similarity and makes it explicit, to enable data and technology sharing
across diverse applications.
The SKOS data model provides a standard, low-cost migration path for
porting existing knowledge organization systems to the Semantic Web. SKOS
also provides a lightweight, intuitive language for developing and sharing
new knowledge organization systems. It may be used on its own, or in
combination with formal knowledge representation languages such as the Web
Ontology language (OWL).
This document is the normative specification of the Simple Knowledge
Organization System. It is intended for readers who are involved in the
design and implementation of information systems, and who already have a good
understanding of Semantic Web technology, especially RDF and OWL.
For an informative guide to using SKOS, see the [
SKOS-PRIMER
].
Synopsis
Using SKOS,
concepts
can be
identified using URIs,
labeled
with
lexical strings in one or more natural languages, assigned
notations
(lexical codes),
documented
with various types of note,
linked to other concepts
and
organized into informal hierarchies and association networks, aggregated into
concept schemes
, grouped into labeled
and/or ordered
collections
, and
mapped
to concepts in other schemes.
show quick access panel
Status of This Document
This section describes the status of this document at the time of its
publication. Other documents may supersede this document. A list of current
W3C publications and the latest revision of this technical report can be
found in the
W3C technical reports index
at http://www.w3.org/TR/.
This document is a W3C Recommendation developed by the
Semantic Web Deployment
Working Group
, part of the
W3C Semantic Web
Activity
. This document reflects an editorial change arising during
the Proposed Recommendation review: a non-normative example and
preceding text was removed that suggested one means to reference
a system of notation (e.g. a symbolic notation) in a label where
the system of notation does not correspond to a natural language.
This suggestion was deemed inconsistent with IETF
Best Current
Practice 47
on the use of tags for identifying languages.
Users should consider the
SKOS Extension vocabulary
for support of alternate systems of notation.
An
implementation report
documents known uses of SKOS
during the Candidate Recommendation review period. An updated
SKOS Primer
is being
published concurrently with this Recommendation.
Changes Since
15 June 2009 Proposed Recommendation
Removed final paragraph and example from section 6.5.4
on use of private use language sub-tags.
Editorial changes to references section and citation of
SKOS Primer.
Comments on this document may be sent to
public-swd-wg@w3.org
with
public archive
This document was produced by a group operating under the
5 February 2004 W3C Patent Policy
. W3C maintains a
public list of any patent disclosures
made in connection with
the deliverables of the group; that page also includes instructions
for disclosing a patent. An individual who has actual knowledge of
a patent which the individual believes contains
Essential Claim(s)
must disclose the information in accordance with
section 6 of the W3C Patent Policy
This document has been reviewed by W3C Members, by software developers, and by other W3C groups and interested parties, and is endorsed by the Director as a W3C Recommendation. It is a stable document and may be used as reference material or cited from another document. W3C's role in making the Recommendation is to draw attention to the specification and to promote its widespread deployment. This enhances the functionality and interoperability of the Web.
Quick Access Panel [
hide
Full Table of Contents
Classes
skos:Collection
skos:Concept
skos:ConceptScheme
skos:OrderedCollection
Properties
skos:altLabel
skos:broadMatch
skos:broader
skos:broaderTransitive
skos:changeNote
skos:closeMatch
skos:definition
skos:editorialNote
skos:exactMatch
skos:example
skos:hasTopConcept
skos:hiddenLabel
skos:historyNote
skos:inScheme
skos:mappingRelation
skos:member
skos:memberList
skos:narrowMatch
skos:narrower
skos:narrowerTransitive
skos:notation
skos:note
skos:prefLabel
skos:related
skos:relatedMatch
skos:scopeNote
skos:semanticRelation
skos:topConceptOf
Sections
1. Introduction
2. Namespace and Vocabulary
3. The skos:Concept Class
4. Concept Schemes
5. Lexical Labels
6. Notations
7. Documentation Properties
8. Semantic Relations
9. Concept Collections
10. Mapping Properties
11. References
Appendices
A. SKOS Properties and Classes
B. SKOS eXtension for Labels (SKOS-XL)
C. SKOS and SKOS-XL Namespace Documents
D. SKOS Namespace
Table of Contents
show quick access panel
1. Introduction
1.1. Background and Motivation
1.2. SKOS Overview
1.3. SKOS, RDF and OWL
1.4. Consistency and Integrity
1.5. Inference, Dependency and the Open-World
Assumption
1.6. Design Rationale
1.7. How to Read this Document
1.7.1. Formal Definitions
1.7.2. URI Abbreviations
1.7.3. Examples
1.8. Conformance
2. SKOS Namespace and Vocabulary
3. The skos:Concept Class
3.1. Preamble
3.2. Vocabulary
3.3. Class & Property Definitions
3.4. Examples
3.5. Notes
3.5.1. SKOS Concepts, OWL Classes and OWL
Properties
4. Concept Schemes
4.1. Preamble
4.2. Vocabulary
4.3. Class & Property Definitions
4.4. Integrity Conditions
4.5. Examples
4.6. Notes
4.6.1. Closed vs. Open Systems
4.6.2. SKOS Concept Schemes and OWL
Ontologies
4.6.3. Top Concepts and Semantic
Relations
4.6.4. Scheme Containment and Semantic
Relations
4.6.5. Domain of skos:inScheme
5. Lexical Labels
5.1. Preamble
5.2. Vocabulary
5.3. Class & Property Definitions
5.4. Integrity Conditions
5.5. Examples
5.6. Notes
5.6.1. Domain of SKOS Lexical Labeling
Properties
5.6.2. Range of SKOS Lexical Labeling
Properties
5.6.3. Defining Label Relations
5.6.4. Alternates Without Preferred
5.6.5. Labeling and Language Tags
6. Notations
6.1. Preamble
6.2. Vocabulary
6.3. Class & Property Definitions
6.4. Examples
6.5. Notes
6.5.1. Notations, Typed Literals and
Datatypes
6.5.2. Multiple Notations
6.5.3. Unique Notations in Concept
Schemes
6.5.4. Notations and Preferred Labels
6.5.5. Domain of skos:notation
7. Documentation Properties (Note Properties)
7.1. Preamble
7.2. Vocabulary
7.3. Class & Property Definitions
7.4. Examples
7.5. Notes
7.5.1. Domain of the SKOS Documentation
Properties
7.5.2. Range of the SKOS
Documentation Properties
8. Semantic Relations
8.1. Preamble
8.2. Vocabulary
8.3. Class & Property Definitions
8.4. Integrity Conditions
8.5. Examples
8.6. Notes
8.6.1. Sub-Property Relationships
8.6.2. Domain and Range of SKOS Semantic
Relation Properties
8.6.3. Symmetry of skos:related
8.6.4. skos:related and Transitivity
8.6.5. skos:related and Reflexivity
8.6.6. skos:broader and Transitivity
8.6.7. skos:broader and Reflexivity
8.6.8. Cycles in the Hierarchical Relation
(skos:broaderTransitive and Reflexivity)
8.6.9. Alternate Paths in the Hierarchical
Relation
8.6.10. Disjointness of skos:related and
skos:broaderTransitive
9. Concept Collections
9.1. Preamble
9.2. Vocabulary
9.3. Class & Property Definitions
9.4. Integrity Conditions
9.5. Examples
9.6. Notes
9.6.1. Inferring Collections from Ordered
Collections
9.6.2. skos:memberList Integrity
9.6.3. Nested Collections
9.6.4. SKOS Concepts, Concept Collections and
Semantic Relations
10. Mapping Properties
10.1. Preamble
10.2. Vocabulary
10.3. Class & Property Definitions
10.4. Integrity Conditions
10.5. Examples
10.6. Notes
10.6.1. Mapping Properties, Semantic Relation
Properties and Concept Schemes
10.6.2. Clashes Between Hierarchical and
Associative Links
10.6.3. Mapping Properties and
Transitivity
10.6.4. Mapping Properties and
Reflexivity
10.6.5. Cycles and Alternate Paths Involving
skos:broadMatch
10.6.6. Cycles Involving skos:exactMatch and
skos:closeMatch
10.6.7. Sub-Property Chains Involving
skos:exactMatch
10.6.8. skos:closeMatch, skos:exactMatch,
owl:sameAs, owl:equivalentClass, owl:equivalentProperty
11. References
12. Acknowledgments
Appendix A. SKOS Properties and Classes
A.1. Classes in the SKOS Data Model
A.2. Properties in the SKOS Data Model
Appendix B. SKOS eXtension for Labels (SKOS-XL)
B.1. SKOS-XL Namespace and Vocabulary
B.2. The skosxl:Label Class
B.2.1. Preamble
B.2.2. Class and Property
Definitions
B.2.3. Examples
B.2.4. Notes
B.2.4.1. Identity and
Entailment
B.2.4.2. Membership of Concept
Schemes
B.3. Preferred, Alternate and Hidden
skosxl:Labels
B.3.1. Preamble
B.3.2. Class and Property
Definitions
B.3.3. Examples
B.3.4. Notes
B.3.4.1. Dumbing-Down to SKOS
Lexical Labels
B.3.4.2. SKOS-XL Labeling
Integrity
B.4. Links Between skosxl:Labels
B.4.1. Preamble
B.4.2. Class and Property
Definitions
B.4.3. Examples
B.4.4. Notes
B.4.4.1. Refinements of this
Pattern
Appendix C. SKOS and SKOS-XL Namespace Documents
Appendix D. SKOS Namespace: a historical note
1. Introduction
1.1. Background and Motivation
The Simple Knowledge Organization System is a data-sharing standard,
bridging several different fields of knowledge, technology and practice.
In the library and information sciences, a long and distinguished heritage
is devoted to developing tools for organizing large collections of objects
such as books or museum artifacts. These tools are known generally as
"knowledge organization systems" (KOS) or sometimes as "controlled structured
vocabularies". Several similar yet distinct traditions have emerged over
time, each supported by a community of practice and set of agreed standards.
Different families of knowledge organization systems, including thesauri,
classification schemes, subject heading systems, and taxonomies are
widely recognized and applied in both modern and traditional information
systems. In practice it can be hard to draw an absolute distinction between
thesauri and classification schemes or taxonomies, although some
properties can be used to broadly characterize these different families (see
e.g., [
BS8723-3
]).
The important point for SKOS is that, in addition to their unique features,
each of these families shares much in common, and can often be used in
similar ways [
SKOS-UCR
].
However, there is currently no widely deployed standard for
representing these knowledge organization systems as data and exchanging them
between computer systems.
The W3C's Semantic Web Activity [
SW
] has stimulated a new field of integrative research and
technology development, at the boundaries between database systems, formal
logic and the World Wide Web. This work has led to the development of
foundational standards for the Semantic Web. The Resource Description
Framework (RDF) provides a common data abstraction and syntax for the Web [
RDF-PRIMER
]. The
RDF Vocabulary Description language (RDFS) and the Web Ontology language
(OWL) together provide a common data modeling (schema) language for data in
the Web [
RDFS
] [
OWL-GUIDE
]. The
SPARQL Query Language and Protocol provide a standard means for interacting
with data in the Web [
SPARQL
].
These technologies are being applied across diverse applications, because
many applications require a common framework for publishing, sharing,
exchanging and integrating ("joining up") data from different sources. The
ability to link data from different sources is motivating many projects,
as different communities seek to exploit the hidden value in data
previously spread across isolated sources.
One facet of the Semantic Web vision is the hope of better organizing the
vast amounts of unstructured (i.e., human-readable) information in the Web,
providing new routes to discovering and sharing that information. RDFS and
OWL are formally defined knowledge representation languages, providing ways
of expressing meaning that are amenable to computation, meaning that
complements and gives structure to information already present in the Web [
RDF-PRIMER
] [
OWL-GUIDE
]. However, to actually apply these technologies over large bodies of information
requires the construction of detailed maps of particular domains of
knowledge, in addition to the accurate description (i.e., annotation or
cataloging) of information resources on a large scale, much of which cannot
be done automatically. The accumulated experience and best practices in the
library and information sciences regarding the organization of information
and knowledge are obviously complementary and applicable to this vision, as
are the many existing knowledge organization systems already developed and in
use, such as the Library of Congress Subject Headings [
LCSH
] or the United Nations Food and
Agriculture Organization's AGROVOC Thesaurus [
AGROVOC
].
The Simple Knowledge Organization System therefore aims to provide a
bridge between different communities of practice within the library and
information sciences involved in the design and application of knowledge
organization systems. In addition, SKOS aims to provide a bridge between
these communities and the Semantic Web, by transferring existing models of
knowledge organization to the Semantic Web technology context, and by
providing a low-cost migration path for porting existing knowledge
organization systems to RDF.
Looking to the future, SKOS occupies a position between the exploitation
and analysis of unstructured information, the informal and socially-mediated
organization of information on a large scale, and the formal representation
of knowledge. By making the accumulated experience and
wisdom of knowledge organization in the library and information sciences
accessible, applicable and transferable to the technological context
of the Semantic Web, in a way that is complementary to existing Semantic Web
technology (and in particular formal systems of knowledge representation such
as OWL), it is hoped that SKOS will enable many new and valuable applications, and will also
lead to new integrative lines of research and development in both technology
and practice.
1.2. SKOS Overview
The Simple Knowledge Organization System is a common data model for
knowledge organization systems such as thesauri, classification schemes,
subject heading systems and taxonomies. Using SKOS, a knowledge organization
system can be expressed
as machine-readable data
. It can
then be exchanged between computer applications and published in a
machine-readable format in the Web.
The SKOS data model is formally defined in this specification as an OWL
Full ontology [
OWL-SEMANTICS
]. SKOS data are expressed as RDF
triples [
RDF-CONCEPTS
], and may be encoded using any
concrete RDF syntax (such as RDF/XML [
RDF-XML
] or Turtle [
TURTLE
]). For more on the relationships
between SKOS, RDF and OWL, see the next sub-section below.
The SKOS data model views a knowledge organization system as a
concept scheme
comprising a set of
concepts
. These SKOS concept schemes and SKOS concepts are
identified by URIs, enabling anyone to refer to them unambiguously from any
context, and making them a part of the World Wide Web. See
Section 3. The skos:Concept Class
for more on
identifying and describing SKOS concepts, and
Section 4.
Concept Schemes
for more on concept schemes.
SKOS concepts can be
labeled
with any number of lexical
(UNICODE) strings, such as "romantic love" or "れんあい", in any given
natural language, such as English or Japanese (written here in hiragana). One of these labels
in any given language can be indicated as the preferred label for that
language, and the others as alternative labels. Labels may also be "hidden",
which is useful where a knowledge organization system is being queried
via a text index. See
Section 5. Lexical Labels
for
more on the SKOS lexical labeling properties.
SKOS concepts can be assigned one or more
notations
which are lexical codes used to uniquely identify the concept within the
scope of a given concept scheme. While URIs are the preferred means of
identifying SKOS concepts within computer systems, notations provide a bridge
to other systems of identification already in use such as classification
codes used in library catalogs. See
Section 6.
Notations
for more on notations.
SKOS concepts can be
documented
with notes of various
types. The SKOS data model provides a basic set of documentation properties,
supporting scope notes, definitions and editorial notes, among others. This
set is not meant to be exhaustive, but rather to provide a framework that can
be extended by third parties to provide support for more specific types of
note. See
Section 7. Documentation Properties
for more
on notes.
SKOS concepts can be
linked
to other SKOS concepts via
semantic relation properties. The SKOS data model provides support for
hierarchical and associative links between SKOS concepts. Again, as with any
part of the SKOS data model, these can be extended by third parties to
provide support for more specific needs. See
Section 8. Semantic Relations
for more on
linking SKOS concepts.
SKOS concepts can be grouped into
collections
, which can
be labeled and/or ordered. This feature of the SKOS data model is intended to
provide support for node labels within thesauri, and for situations where the
ordering of a set of concepts is meaningful or provides some useful
information. See
Section 9. Concept Collections
for more on collections.
SKOS concepts can be
mapped
to other SKOS concepts in
different concept schemes. The SKOS data model provides support for four
basic types of mapping link: hierarchical, associative, close equivalent and
exact equivalent. See
Section 10. Mapping Properties
for more on mapping.
Finally, an optional extension to SKOS is defined in
Appendix B. SKOS eXtension for Labels (SKOS-XL)
. SKOS-XL provides more
support for identifying, describing and linking lexical entities.
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 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. The RDF graph
below (in [
TURTLE
as discussed in
Section 1.7.3
) expresses some facts
about a thesaurus.
rdf:type skos:Concept ;
skos:prefLabel "love"@en ;
skos:altLabel "adoration"@en ;
skos:broader ;
skos:inScheme .