SKOS Simple Knowledge Organization System
Reference

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 "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. [@@REF-BS8723-X]). 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.

The W3C's Semantic Web Activity [@@REF-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 [@@REF-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 [@@REF-RDFS] [@@REF-OWL-GUIDE]. The SPARQL Query language and Protocol provide a standard means for interacting with data in the Web [@@REF-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. This ability to "join up" data from different sources is motivating many projects, as different communities seek to exploit the hidden value in linking 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 [@@REF-RDF-PRIMER] [@@REF-OWL-GUIDE]. They go a long way towards supporting that vision, but the story doesn't end there. 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 is 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 [@@REF-LCSH] or the United Nations Food and Agriculture Organization's Agrovoc Thesaurus [@@REF-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. It is hoped that, by making the accumulated experience and wisdom of knowledge organization in the library and information sciences accessible, applicable within 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), 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. What is SKOS?

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 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 [@@REF-OWL-SEMANTICS]. SKOS data are expressed as RDF triples, and may be encoded using any concrete RDF syntax (such as RDF/XML [@@REF-RDF-XML] or Turtle [@@REF-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 Hiragana. One of these labels in any given language can be indicated as the "preferred" label for that language, and the others as "alternate" labels. Labels may also be "hidden", which is useful e.g. 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 (also known as classification codes). 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 three basic types of mapping link: hierarchical, associative and exact equivalent. See Section 10. Mapping Properties for more on mapping.

Finally, an optional extension to SKOS is defined in Appendix A. SKOS eXtension for Labels (XL). XL provides more support for identifying, describing and linking lexical entities.

1.4. SKOS, RDF and OWL

As mentioned above, the SKOS data model is formally defined as an OWL Full ontology. I.e. SKOS is itself an OWL Full ontology. 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.

I.e. it is appropriate (if one has the time, effort and need) to manually re-engineer a thesaurus or classification scheme as a formal OWL ontology, using the "concepts" of the thesaurus as a starting point for creating classes, properties and individuals in the ontology, and using the informal hierarchies and association networks of the thesaurus as a starting point for creating the axioms and facts of the ontology. However, OWL is a formal ontology language, and it does not by itself provide a natural or suitable data model for expressing a thesaurus or classification scheme. I.e. it is not appropriate to express the "concepts" of a thesaurus or classification scheme directly as classes of an ontology, nor to express the informal (broader/narrower) hierarchy of a thesaurus directly as a set of class subsumption axioms. The reason for this is that, because a thesaurus or classification scheme has not been developed with formal semantics in mind, but rather as an informal or semi-formal aid to navigation and information retrieval, expressing a thesaurus hierarchy directly as a set of ontology classes with subsumption axioms typically leads to a number of inappropriate or nonsensical conclusions.

OWL does, however, provide a powerful data modeling language. We can, therefore, use OWL to construct a data model which is appropriate to the level of formality in a thesaurus or classification scheme. 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, as are typically expressed in a formal ontology.

SKOS data are then expressed as RDF triples. For example, the RDF graph below expresses some facts about a thesaurus.

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

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

<C> rdf:type skos:ConceptScheme ;
  dc: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 users 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, and can often be ignored without any serious repercussions. 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.

1.5. Consistency and Integrity

Under the RDF and OWL Full semantics, the formal meaning (interpretation) of an RDF graph is a truth value [@@REF-RDF-SEMANTICS] [@@REF-OWL-SEMANTICS]. I.e. an RDF graph is interpreted as either true or false.

In general, an RDF graph is said to be inconsistent if it cannot possibly be true. In other words, an RDF graph is inconsistent if it contains a contradiction.

Using the RDF and RDFS vocabularies alone, it is virtually impossible to make a contradictory statement. When the OWL vocabulary is used as well, there are many ways to state a contradiction. For example, consider the RDF graph below.

<Foo> rdf:type owl:Class .
<Bar> rdf:type owl:Class .
<Foo> owl:disjointWith <Bar> .
<foobar> rdf:type <Foo> , <Bar> .

The graph states that <Foo> and <Bar> are both classes, and that they are disjoint, i.e. that they do not have any members in common. This is contradicted by the statement that <foobar> has type both <Foo> and <Bar>. There is no OWL Full interpretation which can satisfy this graph, and therefore this graph is not OWL Full consistent.

When OWL Full is used as a knowledge representation language, the notion of inconsistency is useful because it reveals contradictions within the axioms and facts that are asserted in an ontology. By resolving these inconsistencies we learn more about a domain of knowledge, and come to a better model of that domain from which interesting and valid inferences can be drawn.

When OWL Full is used as a data modeling (i.e. schema) language, the notion of inconsistency is again useful, but in a different way. Here we are not concerned with the logical consistency of human knowledge itself. We are simply interested in formally defining a data model, so that we can establish with certainty whether or not some given data conforms to or "fits" with the given data model. If the data is inconsistent with respect to the data model, then it does not "fit".

Here, we are not concerned with whether or not some given data has any correspondence with the "real world", i.e. whether it is true or false in any absolute sense. We are simply interested in whether or not the data fits the data model, because interoperability within a given class of applications depends on data conforming to a common data model.

Another way to express this view is via the notion of integrity. Integrity conditions are statements within the formal definition of a data model, which are used to establish whether or not given data is consistent with respect to the data model.

For example, the statement that <Foo> and <Bar> are disjoint classes can be viewed as an integrity condition on a data model. Given this condition, the data below is then not (OWL Full) consistent.

<foobar> rdf:type <Foo> , <Bar> .

The definition of the SKOS data model given in this document contains a limited number of statements that are intended as integrity conditions. These integrity conditions are included to promote interoperability, by defining the circumstances under which data is not consistent with respect to the SKOS data model. Tools can then be implemented which "check" whether some or all of these integrity conditions are met for given data, and therefore whether the data "fits" the SKOS data model.

These integrity conditions are part of the formal definition of the classes and properties of the SKOS data model, however they are presented separately from other parts of the formal definition because they serve a different purpose. Integrity conditions serve primarily to establish whether given data is consistent with the SKOS data model. All other statements within the definition of the SKOS data model serve only to support logical inferences (see also the next sub-section).

Integrity conditions are defined for the SKOS data model in a way that is independent of strategies for their implementation, in so far as that is possible. This is because there are several different ways in which a procedure to find inconsistencies with the SKOS data model could be implemented. For example, inconsistencies could be found using an OWL reasoner. Alternatively, some inconsistencies could be found by searching for specific patterns within the data, or by a hybrid strategy (draw inferences using an RDFS or OWL reasoner, then search for patterns in the inferred graph).

The integrity conditions on the SKOS data model are fewer than might be expected, especially for those used to working within the "closed world" of database systems. See also the next sub-section, and the notes in sections 3-11 below.

1.6. Inference, Dependency and the Open-World Assumption

This document defines the SKOS data model as an OWL Full ontology. There are other, alternate ways in which the SKOS data model could have been defined, for example as an entity-relationship model, or a UML class model. Although OWL Full as a data modeling language appears intuitively similar in many ways to these other modeling approaches, there is an important fundamental distinction.

RDF and OWL Full are designed for systems in which data may be widely distributed (e.g. the Web). As such a system becomes larger, it becomes both impractical and virtually impossible to "know" where all of the data in the system is located. Therefore, one cannot generally assume that data obtained from such a system is "complete". I.e. if some data appears to be "missing", one has to assume, in general, that the data might exist somewhere else in the system. This assumption, roughly speaking, is known as the "open world" assumption [@@REF-OWL-GUIDE].

This means in practice that, for a data model defined as an OWL Full ontology, some definitions can have a counter-intuitive meaning. No conclusions can be drawn from "missing" data, and removing something will never make the remaining data inconsistent.

For example, in Section 8 below, the property skos:semanticRelation is defined to have domain and range skos:Concept. These statements are not integrity conditions. I.e. the graph below is perfectly consistent with the SKOS data model, despite the fact that <A> and <B> have not been explicitly declared as instances of skos:Concept.

<A> skos:semanticRelation <B> .

In fact, the domain and range definitions give license to inferences. In this case, the graph above (RDFS and OWL Full) entails the following graph. I.e. the following graph can be deduced or inferred.

<A> rdf:type skos:Concept .
<B> rdf:type skos:Concept .

In the SKOS data model, most statements of definition are not integrity conditions, but are statements of logical dependency between different elements of the data model, which under the open-world assumption give license to a number of simple inferences. For example, in section 7 below, skos:broader and skos:narrower are defined as inverse properties. This statement means that

<A> skos:narrower <B> .

entails

<B> skos:broader <A> .

Both of these two graphs are, by themselves, consistent with the SKOS data model.

Knowledge organization systems such as thesauri and classification schemes are applied in a wide range of situations, and an individual knowledge organization system can be used in many different information systems. By defining the SKOS data model as an OWL Full ontology, the Semantic Web can then be used as a medium for publishing, exchanging, sharing and "joining up" data involving these knowledge organization systems. For this reason, for the expressiveness of OWL Full as a data modeling language, and for the possibility of then using thesauri, classification schemes etc. side-by-side with formal ontologies, OWL Full has been used to define the SKOS data model. The "open world" assumption is therefore a fundamental premise of the SKOS data model, and should be borne in mind when reading this document.

See also [@@REF-RDF-PRIMER] and [@@OWL-GUIDE].

1.7. How to Read this Document

This document formally defines the Simple Knowledge Organization System data model as an OWL Full ontology. The "elements" of the SKOS data model are OWL classes and properties, and a Uniform Resource Identifier (URI) is provided for each of these classes and properties so that they may be used unambiguously in the Web. This set of URIs comprises the SKOS vocabulary.

The complete SKOS vocabulary is given in section 2 below. Sections 3 to 10 then formally define the SKOS data model. The definition of the data model is broken down into a number of sections purely for convenience. Each of these sections 3 to 10 follows a common layout:

• Preamble — the main ideas covered in the section are introduced informally.
• Vocabulary — URIs from the SKOS vocabulary which are defined in the section are given.
• Class & Property Definitions — the logical characteristics and interdependencies between the classes and properties denoted by those URIs are formally defined.
• Integrity Conditions — if there are any integrity conditions, those are given next.
• Examples — some canonical examples are given, both of data which is consistent with the SKOS data model, and (where appropriate) of data which is not consistent with the SKOS data model.
• Notes — any further notes and discussion are presented.

1.7.1. Formal Definitions

Most of the class and property definitions and integrity conditions stated in this document could be stated as RDF triples, using the RDF, RDFS and OWL vocabularies. However, a small number cannot, either because of limitations in the expressiveness of OWL Full or lack of standard URI for some class. To improve the overall readability of this document, rather than mix RDF triples and other notations, the formal definitions and integrity conditions are stated throughout using prose.

The style of this prose generally follows the style used in [@@REF-RDFS], and should be clear to a reader with a working knowledge of RDF and OWL.

So, for example, "ex:Foo is an instance of owl:Class" means

ex:Foo rdf:type owl:Class .

"ex:p and ex:q are each instances of owl:ObjectProperty" means

ex:p rdf:type owl:ObjectProperty .
ex:q rdf:type owl:ObjectProperty .

"ex:p is a sub-property of ex:q" means

ex:p rdfs:subPropertyOf ex:q .

"the rdfs:range of ex:p is the class ex:Foo" means

ex:p rdfs:range ex:Foo .

etc.

Where some formal aspects of the SKOS data model cannot be stated as RDF triples using either RDF, RDFS or OWL vocabularies, it should be clear to a reader with a basic understanding of the RDF and OWL semantics how these statements might be translated into formal conditions on the interpretation of an RDF vocabulary.

For example, from Section 5, "A resource has no more than one value of skos:prefLabel per language" means for any resource x, no two members of the set { y | <x,y> is in IEXT(I(skos:prefLabel)) } share the same language tag (where I and IEXT are functions as defined in [@@REF-RDF-SEMANTICS]).

1.7.2. URI Abbreviations

Full URIs are cited in the text of this document in monospace font, enclosed by angle brackets. For example, <http://example.org/ns/example>. Relative URIs are cited in the same way, and are relative to the base URI <http://example.org/ns/>. For example, <example> and <http://example.org/ns/example> are the same URI.

URIs are also cited in the text of this document in an abbreviated form. Abbreviated URIs are cited in monospace font without angle brackets, and should be expanded using the table of abbreviations below.

So, for example, skos:Concept is an abbreviation of <http://www.w3.org/2008/05/skos#Concept>.

1.7.3. Examples

Examples of RDF graphs are given using the Terse RDF Triple language (Turtle) [@@REF-TURTLE]. All examples assume that they are preceded by the following prefix and URI base directives:

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix skos: <http://www.w3.org/2008/05/skos#> .
@base <http://example.org/ns/> .

Therefore, the example given below

is equivalent to the following Turtle document

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix skos: <http://www.w3.org/2008/05/skos#> .
@base <http://example.org/ns/> .
    
<MyConcept> rdf:type skos:Concept .

which is equivalent to the following RDF/XML document [@@REF-RDF-XML]

<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF 
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" 
    xmlns:rdfs="http://www.w3.org/1999/02/22-rdf-syntax-ns#" 
    xmlns:skos="http://www.w3.org/2008/05/skos#"
    xmlns:owl="http://www.w3.org/2002/07/owl#"
    xml:base="http://example.org/ns/">
    
    <skos:Concept rdf:about="MyConcept"/>
    
</rdf:RDF>

which is equivalent to the following N-TRIPLES document [@@REF-NTRIPLES]

<http://example.org/ns/MyConcept> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2008/05/skos#Concept> .

Note the use in Turtle of the ";" and "," characters to abbreviate multiple triples with the same subject or predicate.

1.8. Conformance

This specification does not define a formal notion of conformance.

However, an RDF graph will be inconsistent with the SKOS data model if that graph and the SKOS data model (as defined formally below) taken together lead to a logical contradiction.

2. SKOS Namespace and Vocabulary

The SKOS namespace URI is:

• http://www.w3.org/2008/05/skos#

The SKOS vocabulary is a set of URIs, given in the left-hand column in the table below.

All URIs in the SKOS vocabulary are constructed by appending a local name (e.g. "prefLabel") to the SKOS namespace URI.

See also the SKOS overview in Appendix B and the quick access panel.

3. The skos:Concept Class

3.1. Preamble

The class skos:Concept is the class of SKOS concepts.

A SKOS concept can be viewed as an idea or notion; a unit of thought. However, what constitutes a "unit of thought" is subjective, and this definition is meant to be suggestive, rather than restrictive.

The notion of a SKOS concept is useful when describing the conceptual or intellectual structure of a knowledge organization system, and when referring to specific ideas or meanings established within a KOS.

Note that, because SKOS is designed to be a vehicle for representing semi-formal KOS, such as thesauri and classification schemes, a certain amount of flexibility has been built in to the formal definition of this class.

See the SKOS Primer for more examples of identifying and describing SKOS concepts.

3.2. Vocabulary

3.3. Class & Property Definitions

3.4. Examples

The graph below states that <MyConcept> is a SKOS concept (i.e. an instance of skos:Concept).

3.5. Notes

3.5.1. SKOS Concepts, OWL Classes and OWL Properties

This specification does not make any statement about the formal relationship between the class of SKOS concepts and the class of OWL classes. The decision not to make any such statement has been made to allow applications the freedom to explore different design patterns for working with SKOS in combination with OWL.

For example, in the graph below, <MyConcept> is an instance of skos:Concept and an instance of owl:Class.

This example is consistent with the SKOS data model. However, note that it is not compatible with OWL DL, because the URI <MyConcept> has been used to denote both a class and an individual [@@REF-OWL-SEMANTICS]. To work within the OWL DL language, take care not to use the same URI to denote both an OWL class and a SKOS concept.

Similarly, this specification does not make any statement about the formal relationship between the class of SKOS concepts and the class of OWL properties.

For example, in the graph below, <MyConcept> is an instance of skos:Concept and an instance of owl:ObjectProperty.

This example is consistent with the SKOS data model. However, it is not compatible with OWL DL, because the URI <MyConcept> has been used to denote both an object property and an individual [@@REF-OWL-SEMANTICS].

4. Concept Schemes

4.1. Preamble

A SKOS concept scheme can be viewed as an aggregation of one or more SKOS concepts. Semantic relationships (links) between those concepts may also be viewed as part of a concept scheme. This definition is, however, meant to be suggestive rather than restrictive, and there is a certain amount of flexibility in the formal semantics stated below (see also the notes below).

The notion of a concept scheme is useful when dealing with data from an unknown source, and when dealing with data that describes two or more different knowledge organization systems.

See the SKOS Primer for more examples of identifying and describing concept schemes.

4.2. Vocabulary

4.3. Class & Property Definitions

4.4. Integrity Conditions

4.5. Examples

The graph below describes a concept scheme with two SKOS concepts, one of which is a top-level concept in that scheme.

4.6. Notes

4.6.1. Closed vs. Open Systems

The notion of an individual SKOS concept scheme corresponds roughly to the notion of an individual thesaurus, classification scheme, subject heading system or other knowledge organization system.

However, in most current information systems, a thesaurus or classification scheme is treated as a closed system — conceptual units defined within that system cannot take part in other systems (although they can be mapped to units in other systems).

Although SKOS does take a similar approach, there are no conditions preventing a SKOS concept from taking part in zero, one, or more than one concept scheme.

So, for example, in the graph below the SKOS concept <MyConcept> takes part in two different concept schemes — this is consistent with the SKOS data model.

This flexibility is desirable because it allows, for example, new concept schemes to be described by linking two or more existing concept schemes together.

Also, note that there is no way to close the boundary of a concept scheme. So, while it is possible using skos:inScheme to say that SKOS concepts X, Y and Z take part in concept scheme A, there is no way to say that only X, Y and Z take part in A.

Therefore, while SKOS can be used to describe a concept scheme, SKOS does not provide any mechanism to completely define a concept scheme.

4.6.2. SKOS Concept Schemes and OWL Ontologies

This specification does not make any statement about the formal relationship between the class of SKOS concept schemes and the class of OWL ontologies. The decision not to make any such statement has been made to allow different design patterns to be explored for using SKOS in combination with OWL [@@REF-OWL-GUIDE].

For example, in the graph below, <MyScheme> is both a concept scheme and an OWL ontology. This is consistent with the SKOS data model.

4.6.3. SKOS Concept Schemes and Named RDF Graphs

This specification does not make any statement about the formal relationship between the class of SKOS concept schemes and the class of named RDF Graphs. The decision not to make any such statement has been made to allow different design patterns to be explored for using SKOS with query languages such as SPARQL [@@REF-SPARQL].

4.6.4. Top Concepts and Semantic Relations

The property skos:hasTopConcept is, by convention, used to link a concept scheme to the SKOS concept(s) which are topmost in the hierarchical relations for that scheme. However, note that there are no integrity conditions enforcing this convention. Therefore, the graph below, whilst not strictly adhering to the usage convention for skos:hasTopConcept, is nevertheless consistent with the SKOS data model.

5. Lexical Labels

5.1. Preamble

A lexical label is a string of UNICODE characters, such as "romantic love" or "れんあい", in a given natural language, such as English or Japanese Hiragana.

The Simple Knowledge Organization System provides some basic vocabulary for associating lexical labels with resources of any type. In particular, SKOS enables a distinction to be made between the "preferred", "alternate" and "hidden" lexical labels for any given resource.

The "preferred" and "alternate" labels are useful when generating or creating human-readable representations of a knowledge organization system. These labels provide the strongest clues as to the meaning of a SKOS concept.

The "hidden" labels are useful when a user is interacting with a knowledge organization system via a text-based search function. The user may, for example, enter mis-spelled words when trying to find a relevant concept. If the mis-spelled query can be matched against a "hidden" label, the user will be able to find the relevant concept, but the "hidden" label won't otherwise be visible to the user (so further mistakes aren't encouraged).

Formally, a lexical label is an RDF plain literal [@@REF-RDF-CONCEPTS]. An RDF plain literal is composed of a lexical form, which is a string of UNICODE characters, and an optional language tag, which is a string of characters conforming to the syntax defined by [@@REF-BCP47].

See the @@Primer for more examples of labeling SKOS concepts.

5.2. Vocabulary

5.3. Class & Property Definitions

5.4. Integrity Conditions

N.B. the conditions above assume that each distinct language tag allowed by [@@REF-BCP47] denotes a different "language". E.g. "en", "en-GB" and "en-US" denote three different languages (English, British English and US English respectively). E.g. "ja", "ja-Hani", "ja-Hira", "ja-Kana" and "ja-Latn" denote five different languages (Japanese, Japanese Kanji, Japanese Hiragana, Japanese Katakana and Japanese Rōmaji respectively).

5.5. Examples

The following graph is consistent, and illustrates the provision of lexical labels in two different languages (French and English).

The following graph is consistent, and illustrates the provision of lexical labels in four different languages (Japanese Kanji, Japanese Hiragana, Japanese Katakana and Japanese Rōmaji).

The following graph is not consistent with the SKOS data model, because two different preferred lexical labels have been given for the same language.

The following graph is not consistent with the SKOS data model, because there is a "clash" between the preferred and alternate lexical labels.

The following graph is not consistent with the SKOS data model, because there is a "clash" between alternate and hidden lexical labels.

The following graph is not consistent with the SKOS data model, because there is a "clash" between preferred and hidden lexical labels.

5.6. Notes

5.6.1. Domain of SKOS Lexical Labeling Properties

Note that no domain is stated for skos:prefLabel, skos:altLabel and skos:hiddenLabel. Thus, the effective domain of these properties is the class of all resources (rdfs:Resource).

Therefore, using the properties skos:prefLabel, skos:altLabel and skos:hiddenLabel to label any type of resource is consistent with the SKOS data model.

For example, in the graph below, skos:prefLabel, skos:altLabel and skos:hiddenLabel have been used to label a resource of type owl:Class — this is consistent with the SKOS data model.

This example is, however, incompatible with OWL DL, because a datatype property has been used to make assertions about a class [@@REF-OWL-SEMANTICS].

5.6.2. Range of SKOS Lexical Labeling Properties

Note that the range of skos:prefLabel, skos:altLabel and skos:hiddenLabel is the class of RDF plain literals [@@REF-RDF-CONCEPTS].

By convention, RDF plain literals are always used in the object position of a triple, where the predicate is one of skos:prefLabel, skos:altLabel or skos:hiddenLabel. However, there is nothing in the RDF or OWL Full semantics which prevents the use of a URI or a blank node to denote an RDF plain literal. Therefore, although the graph below does not adhere to the usage convention stated above, it is nevertheless consistent with the SKOS data model.

For an application that needs to identify labels using URIs, consider using the SKOS eXtension for Labels defined in Appendix A.

5.6.3. Alternates Without Preferred

In the graph below, a resource has two alternate lexical labels, but no preferred lexical label. This is consistent with the SKOS data model, and there are no additional entailments which follow from the semantics. However, note that many applications will require a preferred lexical label in order to generate an optimum human-readable display.

5.6.4. Definition of a "Language"

Note how "language" has been defined for the conditions above. Each different language tag permissible by [@@REF-BCP47] is taken to denote a different "language". Therefore the graph below is consistent with the SKOS data model, because "en", "en-US" and "en-GB" are taken to denote different languages.

In the graph below, there is no "clash" between the lexical labeling properties, again because "en" and "en-GB" are taken to denote different languages, and therefore the graph is consistent.

6. Notations

6.1. Preamble

A notation is a string of characters such as "T58.5" or "303.4833" used to uniquely identify a concept within the scope of a given concept scheme.

A notation is different from a lexical label in that a notation is not normally recognizable as a word or sequence of words in any natural language.

This section defines the skos:notation property. This property is used to assign a notation to a concept as a typed literal [@@REF-RDF-CONCEPTS].

6.2. Vocabulary

6.3. Class & Property Definitions

6.4. Examples

The example below illustrates a resource <http://example.com/ns/MyConcept> with a notation whose lexical form is the UNICODE string "303.4833" and whose datatype is denoted by the URI <http://example.com/ns/MyNotationDatatype>.

6.5. Notes

6.5.1. Notations, Typed Literals and Datatypes

A typed literal is a UNICODE string combined with a datatype URI [@@REF-RDF-CONCEPTS].

Typed literals are commonly used to denote values such as integers, floating point numbers and dates, and there are a number of datatypes pre-defined by the XML Schema specification [@@REF-XML-SCHEMA] such as xs:integer, xs:float and xs:date.

For other situations, new datatypes can be defined, and these are commonly called "user-defined datatypes" [@@REF-SWBP-DATATYPES].

By convention, the property skos:notation is only used with a typed literal in the object position of the triple, where the datatype URI denotes a user-defined datatype corresponding to a particular system of notations or classification codes.

For many situations it may be sufficient to simply coin a datatype URI for a particular notation system, and define the datatype informally via a document that describes how the notations are constructed and/or which lexical forms are allowed. Note, however, that it is also possible to define at least the lexical space of a datatype more formally via the XML Schema language, see [@@REF-SWBP-DATATYPES] section 2.

6.5.2. Multiple Notations

There are no constraints on the cardinality of the skos:notation property. A concept may have zero, 1 or more notations.

Where a concept has more than 1 notation, these may be from the same or different notation systems (i.e. datatypes). However, it is not common practice to assign more than one notation from the same notation system (i.e. with the same datatype URI).

6.5.3. Unique Notations in Concept Schemes

By convention, no two concepts in the same concept scheme are given the same notation. If they were, it would not be possible to use the notation to uniquely refer to a concept (i.e. the notation would become ambiguous).

However, this usage convention is not part of the formal SKOS data model. I.e. there are no integrity conditions on the use of skos:notation.

6.5.4. Notations and Preferred Labels

There are no constraints on the combined use of skos:notation and skos:prefLabel. In the example below, the same string is given both as the lexical form of a notation and as a the lexical form of a preferred label.

Typed literals consist of a string of characters and a datatype URI. By convention, skos:notation is only used with typed literals in the object position of the triple.

Plain literals consist of a string of characters and a language tag. By convention, skos:prefLabel (and skos:altLabel and skos:hiddenLabel) are only used with plain literals in the object position of the triple.

There is no such thing as an RDF literal with both a language tag and a datatype URI. I.e. a typed literal does not have a language tag, and a plain literal does not have a datatype URI.

Usually a notation system does not have any correspondance with a natural language — it is language-independent (and hence typed literals are an appropriate representation). However, in some cases a system of notation might be associated with a particular language and/or script. One possible strategy in this situation is to use the private use language sub-tags [@@REF-BCP47] with skos:prefLabel, as illustrated in the example below — this is consistent with the SKOS data model. An alternative strategy is to define one or more sub-properties of skos:prefLabel and/or skos:altLabel.

7. Documentation Properties (Note Properties)

7.1. Preamble

Notes are used to provide information relating to SKOS concepts. There is no restriction on the nature of this information. For example, it could be plain text, hypertext, or an image; it could be a definition, information about the scope of a concept, editorial information, or any other type of information.

There are 7 properties in SKOS for associating notes with concepts, defined formally in this section. For more information on the recommended usage of each of the SKOS documentation properties, see the @@Primer.

These 7 properties are not intended to cover every situation, but rather to be useful in some of the most common situations, and to provide a set of extension points for defining more specific types of note. For more information on recommended best practice for extending SKOS, see the @@Primer.

Three different usage patterns are recommended in the @@Primer for the SKOS documentation properties — "documentation as an RDF literal", "documentation as a related resource description" and "documentation as a document reference". The formal semantics stated in this section are intended to accommodate all three design patterns. See also the notes below.

7.2. Vocabulary

7.3. Class & Property Definitions

7.4. Examples

The graph below gives an example of the "documentation as an RDF literal" pattern.

The graph below gives an example of the "documentation as a related resource description" pattern.

The graph below gives an example of the "documentation as a document reference" pattern.

7.5. Notes

7.5.1. Domain of the SKOS Documentation Properties

Note that no domain is stated for the SKOS documentation properties. Thus, the effective domain for these properties is the class of all resources (rdfs:Resource). Therefore, using the SKOS documentation properties to provide information on any type of resource is consistent with the SKOS data model.

For example, in the graph below, skos:definition has been used to provide a plain text definition for a resource of type owl:Class — this is consistent with the SKOS data model.

This example is, however, incompatible with OWL DL, because an object property has been used to make an assertion about a class [@@REF-OWL-SEMANTICS].

7.5.2. Type & Range of the SKOS Documentation Properties

Note that the basis for the SKOS data model is the RDF-compatible model-theoretic semantics for OWL Full [@@REF-OWL-SEMANTICS]. Under the OWL Full semantics, the class of OWL datatype properties is a sub-class of the class of OWL object properties [@@REF-OWL-REFERENCE]. Therefore, stating that the SKOS documentation properties are all instances of owl:ObjectProperty is the most general statement that can be made, and is consistent with all three usage patterns described in the @@Primer.

Note also that no range is stated for the SKOS documentation properties, and thus the range of these properties is effectively the class of all resources (rdfs:Resource). Under the RDF and OWL Full semantics, everything is a resource, including RDF plain literals.

8. Semantic Relations

8.1. Preamble

SKOS semantic relations are links between SKOS concepts, where the link is inherent in the meaning of the linked concepts.

The Simple Knowledge Organization System distinguishes between two basic categories of semantic relation: hierarchical and associative. A hierarchical link between two concepts indicates that one is in some way more general ("broader") than the other ("narrower"). An associative link between two concepts indicates that the two are inherently "related", but that one is not in any way more general than the other.

The properties skos:broader and skos:narrower are used to assert a hierarchical link between two SKOS concepts. A triple <A> skos:broader <B> asserts that <B>, the object of the triple, is in some way a broader concept than <A>, the subject of the triple. Similarly, a triple <C> skos:narrower <D> asserts that <D>, the object of the triple is, in some way a narrower concept than <C>, the subject of the triple.

Note that the properties skos:broader and skos:narrower are not defined as transitive properties. Applications may wish to make use of the transitive closure of skos:broader (or skos:narrower), for instance to improve search recall through query expansion. For these purposes, transitive super-properties skos:broaderTransitive and skos:narrowerTransitive are provided.

By convention, skos:broader and skos:narrower are only used to assert an immediate (i.e. direct) hierarchical link between two SKOS concepts.

By convention, the properties skos:broaderTransitive and skos:narrowerTransitive are not used to make assertions. Rather, these properties are used to draw inferences about the transitive closure of the hierarchical relation, which is useful e.g. when implementing a simple query expansion algorithm in a search application.

The property skos:related is used to assert an associative link between two SKOS concepts.

For more examples of stating hierarchical and associative links, see the @@Primer.

8.2. Vocabulary

8.3. Class & Property Definitions

8.4. Integrity Conditions

8.5. Examples

The graph below asserts a hierarchical link between <A> and <B> (where <B> is broader than <A>), and an associative link between <A> and <C>, and is consistent with the SKOS data model.

The graph below is not consistent with the SKOS data model, because there is a "clash" between associative links and hierarchical links.

The graph below is not consistent with the SKOS data model, again because there is a "clash" between associative links and hierarchical links.

In the example above, the "clash" is not immediately obvious. The "clash" becomes apparent when inferences are drawn, based on the class and property definitions above, giving the following graph.

The graph below is not consistent with the SKOS data model, again because there is a "clash" between associative links and hierarchical links, which can be inferred from the class and property definitions given above.

8.6. Notes

8.6.1. Sub-Property Relationships

The diagram below illustrates informally the sub-property relationships between the SKOS semantic relation properties.

skos:semanticRelation
 |
 +— skos:related
 |
 +— skos:broaderTransitive
 |    |
 |    +— skos:broader
 |
 +— skos:narrowerTransitive
      |
      +— skos:narrower

8.6.2. Domain and Range of SKOS Semantic Relation Properties

Note that the domain and range of skos:semanticRelation is the class skos:Concept. Because skos:broader, skos:narrower and skos:related are each sub-properties of skos:semanticRelation, the graph in example 27 above entails that <A>, <B> and <C> are each instances of skos:Concept.

8.6.3. Symmetry of skos:related

skos:related is a symmetric property. The example below illustrates an entailment which follows from this condition.

Note that, although skos:related is a symmetric property, this condition does not place any restrictions on sub-properties of skos:related. I.e. sub-properties of skos:related could be symmetric, not symmetric or antisymmetric, and still be consistent with the SKOS data model.

To illustrate this point, in the example below, two new properties which are not symmetric are declared as sub-properties of skos:related. The example, which is consistent with the SKOS data model, also shows some of the entailments which follow.

See also the @@Primer for best practice recommendations on extending SKOS.

8.6.4. Transitivity of skos:related

Note that skos:related is not a transitive property. Therefore, the SKOS data model does not support an entailment as illustrated in the example below.

8.6.5. Reflexivity of skos:related

Note that this specification does not state that skos:related is a reflexive property, neither does it state that skos:related is an irreflexive property.

Because skos:related is not defined as an irreflexive property, the graph below is consistent with the SKOS data model.

However, for many applications that use knowledge organization systems, statements of the form X skos:related X are a potential problem. Where this is the case, an application may wish to search for such statements prior to processing SKOS data, although how an application should handle such statements is not defined in this specification and may vary between applications.

8.6.6. Transitivity of skos:broader

Note that skos:broader is not a transitive property. Similarly, skos:narrower is not a transitive property. Therefore, the SKOS data model does not support an entailment as illustrated in the example below.

However, skos:broader is a sub-property of skos:broaderTransitive, which is a transitive property. Similarly, skos:narrower is a sub-property of skos:narrowerTransitive, which is a transitive property. Therefore the SKOS data model does support the entailment illustrated below.

Note especially that, by convention, skos:broader and skos:narrower are only used to assert immediate (i.e. direct) hierarchical links between two SKOS concepts. By convention, skos:broaderTransitive and skos:narrowerTransitive are not used to make assertions, but are instead used only to draw inferences.

This pattern allows the information about direct hierarchical links to be preserved, which is necessary for many tasks (e.g. building various types of visual representation of a knowledge organization system), whilst also providing a mechanism for conveniently querying the transitive closure of those hierarchical links, which is useful in other situations (e.g. simple query expansion algorithms).

Note also that a sub-property of a transitive property is not necessarily transitive.

See also the note on alternate paths below.

8.6.7. Reflexivity of skos:broader

Note that this specification makes no statements regarding the reflexive characteristics of the skos:broader relationship. It does not state that skos:broader is a reflexive property, neither does it state that skos:broader is an irreflexive property. Thus for any graph and resource <A>, the triple:

may or may not be present. This conservative position allows SKOS to be used to model both KOS where the interpretation of skos:broader is reflexive (e.g. a direct translation of an inferred OWL subclass hierarchy), or KOS where skos:broader could be considered irreflexive (as would be appropriate for most thesauri or classification schemes).

Similarly, there are no assertions made as to the reflexivity or irreflexivity of skos:narrower.

However, for many applications that use knowledge organization systems, statements of the form X skos:broader X or Y skos:narrower Y represent a potential problem. Where this is the case, an application may wish to search for such statements prior to processing SKOS data, although how an application should handle such statements is not defined in this specification and may vary between applications.

8.6.8. Cycles in the Hierarchical Relation (Reflexivity of skos:broaderTransitive)

In the graph below, a cycle has been stated in the hierarchical relation. Note that this graph is consistent with the SKOS data model. I.e. there is no condition requiring that skos:broaderTransitive be irreflexive.

However, for many applications where knowledge organization systems are used, a cycle in the hierarchical relation represents a potential problem. For these applications, computing the transitive closure of skos:broaderTransitive then looking for statements of the form X skos:broaderTransitive X is a convenient strategy for finding cycles in the hierarchical relation. How an application should handle such statements is not defined in this specification and may vary between applications.

8.6.9. Alternate Paths in the Hierarchical Relation

In the graph below, there are two alternate paths from A to C in the hierarchical relation.

In the graph below, there are two alternate paths from A to D in the hierarchical relation.

This is a pattern which arises naturally in "poly-hierarchical" knowledge organization systems.

Both of these graphs are consistent with the SKOS data model. I.e. there is no condition requiring that there be only one path between any two nodes in the hierarchical relation.

8.6.10. Disjointness of skos:related and skos:broaderTransitive

This specification treats the hierarchical and associative relations as fundamentally distinct in nature, and that therefore a "clash" between hierarchical and associative links is not consistent with the SKOS data model. The examples above illustrate some situations in which a "clash" is seen to arise.

This position follows the usual definitions given to hierarchical and associative relations in thesaurus standards [@@REF-ISO2788] [@@REF-BS8273-2], and supports common practice in many existing knowledge organization systems.

Note that this specification takes the stronger position that, not only are the immediate (i.e. direct) hierarchical and associative links disjoint, but associative links are also disjoint with indirect hierarchical links. This is captured formally in the integrity condition asserting that skos:related and skos:broaderTransitive are disjoint properties.

9. Concept Collections

9.1. Preamble

SKOS concept collections are labeled and/or ordered groups of SKOS concepts.

Collections are useful where a group of concepts shares something in common, and it is convenient to group them under a common label, or where some concepts can be placed in a meaningful order.

9.2. Vocabulary

9.3. Class & Property Definitions

9.4. Integrity Conditions

9.5. Examples

The graph below illustrates a SKOS collection with 3 members.

The graph below illustrates an ordered SKOS collection with 3 members.

9.6. Notes

9.6.1. Inferring Collections from Ordered Collections

Statement S33 states the logical relationship between the skos:memberList and skos:member properties. This relationship means that a collection can be inferred from an ordered collection. This is illustrated in the example below.

Note that SKOS does not provide any way to explicitly state that a collection is not ordered.

9.6.2. skos:memberList Integrity

Note that skos:memberList is a functional property, i.e. it does not have more than one value. This is intended to capture within the SKOS data model that it doesn't make sense for an ordered collection to have more than one member list. Unfortunately, there is no way to use this condition as an integrity constraint without explicitly stating that two lists are different objects. In other words, although the graph below is consistent with the SKOS data model, it entails nonsense (a list with two first elements and a forked tail).

9.6.3. Nested Collections

In the example below, a collection is nested within another collection.

This example is consistent with the SKOS data model.

9.6.4. SKOS concepts, Concept Collections and Semantic Relations

In the SKOS data model, skos:Concept and skos:Collection are disjoint classes. The domain and range of the SKOS semantic relation properties is skos:Concept. Therefore, if any of the SKOS semantic relation properties (e.g. skos:narrower) are used to link to or from a collection, the graph will not be consistent with the SKOS data model.

This is illustrated in the example below, which is not consistent with the SKOS data model.

Similarly, the graph below is not consistent with the SKOS data model.

Similarly, the graph below is not consistent with the SKOS data model.

However, the graph below is consistent with the SKOS data model.

This means that, for thesauri and other knowledge organization systems where "node labels" are used within the systematic display for that thesaurus, the appropriate SKOS representation requires careful consideration. Furthermore, where "node labels" are used in the systematic display, it may not always be possible to fully reconstruct the systematic display from a SKOS representation alone. Fully representing all of the information represented in a systematic display of a thesaurus or other knowledge organization system, including details of layout and presentation, is beyond the scope of SKOS.

10. Mapping Properties

10.1. Preamble

The SKOS mapping properties are skos:exactMatch, skos:broadMatch, skos:narrowMatch and skos:relatedMatch. These properties are used to state mapping (alignment) links between concepts in different concept schemes, where the links are inherent in the meaning of the linked concepts.

The property skos:exactMatch is used to link two concepts that are sufficiently similar that they can be used interchangeably in an information retrieval application.

The properties skos:broadMatch and skos:narrowMatch are used to state a hierarchical mapping link between two concepts.

The property skos:relatedMatch is used to state an associative mapping link between two concepts.

See also the notes below.

10.2. Vocabulary

10.3. Class & Property Definitions

10.4. Examples

The graph below asserts an exact mapping link between <A> and <B>.

The graph below asserts a hierarchical mapping link between <A> and <B> (where <B> is broader than <A>), and an associative mapping link between <A> and <C>.

10.5. Notes

10.5.1. Mapping Properties, Semantic Relation Properties and Concept Schemes

By convention, the SKOS mapping properties are only used to link concepts in different concept schemes. However, note that using the SKOS semantic relation properties (skos:broader, skos:narrower, skos:related) to link concepts in different concept schemes is also consistent with the SKOS data model (see Section 8).

The mapping properties skos:broadMatch, skos:narrowMatch and skos:relatedMatch are provided as a convenience, for situations where the provenance of data is known, and it is useful to be able to tell "at a glance" the difference between internal links within a concept scheme and mapping links between concept schemes.

However, using the SKOS mapping properties is no substitute for the careful management of RDF graphs or the use of provenance mechanisms.

The rationale behind this design is that it is hard to draw an absolute distinction between internal links within a concept scheme and mapping links between concept schemes. This is especially true in an open environment where different people might re-organise concepts into concept schemes in different ways. What one person views as two concept schemes with mapping links between, another might view as one single concept scheme with internal links only. This specification allows both points of view to co-exist, which (it is hoped) will promote flexibility and innovation in the re-use of SKOS data in the Web.

There is therefore an intimate connection between the SKOS semantic relation properties and the SKOS mapping properties. The property skos:broadMatch is a sub-property of skos:broader, skos:narrowMatch is a sub-property of skos:narrower, and skos:relatedMatch is a sub-property of skos:related. The full set of sub-property relationships is illustrated below.

skos:semanticRelation
 |
 +- skos:related
 |   |
 |   +- skos:relatedMatch
 |
 +- skos:broaderTransitive
 |   |
 |   +— skos:broader
 |       |
 |       +- skos:broadMatch
 |
 +— skos:narrowerTransitive
     |
     +- skos:narrower
         |
         +- skos:narrowMatch

skos:mappingRelation
 |
 +- skos:exactMatch
 |
 +- skos:relatedMatch
 |
 +- skos:broadMatch
 |
 +- skos:narrowMatch       

Examples below illustrate some entailments which follow from this sub-property tree.

Note also that, because different people might re-organise concepts into concept schemes in different ways, a graph might assert mapping links between concepts in the same concept scheme, and there are no formal integrity conditions in the SKOS data model that would make such a graph inconsistent. E.g. the graph below is consistent with the SKOS data model. However, in practice it is expected that such a graph would only ever arise from the merge of two or more graphs from different sources.

10.5.2. "Clashes" Between Hierarchical and Associative Links

Examples below illustrate "clashes" between hierarchical and associative mapping links, which are not consistent with the SKOS data model (because of the sub-property relationships illustrated above, and because of the data model for SKOS semantic relation properties defined in Section 8).

10.5.3. Transitivity of Mapping Properties

None of the SKOS mapping properties are transitive properties. Therefore, entailments as illustrated in examples below are not supported by the SKOS data model.

Editors' note: The Working Group has recognized that it might be reasonable and useful to define skos:exactMatch as a transitive property. This is recorded as ISSUE-72 in the Working Group's issue process. Comments on this issue are invited, and should be sent in an email to the SWD Working Group mailing list (public-swd-wg@w3.org), starting the subject line with "Comment: ISSUE-72".

10.5.4. Reflexivity of Mapping Properties

None of the SKOS mapping properties are reflexive, neither are they irreflexive.

Because skos:exactMatch, skos:broadMatch and skos:relatedMatch are not irreflexive, the graph below is consistent with the SKOS data model.

However, see also Section 8. on the reflexivity of SKOS semantic relation properties.

10.5.5. "Clashes" with skos:exactMatch

The property skos:exactMatch is not disjoint with any of the other mapping properties. Therefore the graph below is consistent with the SKOS data model.

Editors' note: Intuitively, an exact mapping link asserts something fundamentally different from a hierarchical or associative mapping link. Therefore, it might be reasonable and useful to define skos:exactMatch as disjoint with skos:broadMatch (or skos:broader), and disjoint with skos:relatedMatch (or skos:related). This is recorded as ISSUE-73 in the Working Group's issue process. Comments on this issue are invited, and should be sent in an email to the SWD Working Group mailing list (public-swd-wg@w3.org), starting the subject line with "Comment: ISSUE-73".

10.5.6. Cycles and Alternate Paths Involving skos:broadMatch

There are no formal integrity conditions preventing either cycles or alternate paths in a graph of hierarchical mapping links.

In the graph below there are two cycles involving skos:broadMatch. This graph is consistent with the SKOS data model.

In the graph below there are two alternate paths involving skos:broadMatch. This graph is consistent with the SKOS data model.

See however Section 8. on cycles and alternate paths involving skos:broader.

10.5.7. Property Chains Involving skos:exactMatch

There are no property chain axioms in the SKOS data model involving the skos:exactMatch property. Hence the entailments illustrated below are not supported.

Editors' note: Whether any property chain axioms could reasonably be defined for skos:exactMatch is under discussion within the Working Group. This is recorded as ISSUE-75 in the Working Group's issue process. Comments on this issue are invited, and should be sent in an email to the SWD Working Group mailing list (public-swd-wg@w3.org), starting the subject line with "Comment: ISSUE-75".

Appendix A. SKOS eXtension for Labels (XL)

This appendix defines an OPTIONAL extension to the Simple Knowledge Organization System, called the SKOS eXtension for Labels (XL). This extension provides additional support for identifying, describing and linking lexical entities.

A special class of lexical entities, called xl:Label, is defined. Each instance of this class has a single RDF plain literal form, but two instances of this class are not necessarily the same individual if they share the same literal form.

Three labeling properties, xl:prefLabel, xl:altLabel and xl:hiddenLabel, are defined. These properties are used to label SKOS concepts with instances of xl:Label, and are otherwise analogous to the properties of the same local name defined in SKOS (skos:prefLabel, skos:altLabel and skos:hiddenLabel respectively).

A property xl:labelRelation is defined. This property can be used to assert a direct (binary) link between instances of xl:Label. It is primarily intended as an extension point, to be refined for more specific types of link. No built-in refinements of xl:labelRelation are provided, although some examples of how this could be done are given.

A.1. XL Namespace and Vocabulary

The XL namespace URI is:

• http://www.w3.org/2008/05/skos-xl#

Here the prefix xl: is used as an abbreviation for the XL namespace URI.

The XL vocabulary is the set of URIs given in the left-hand column of the table below.

Here "the SKOS+XL vocabulary" refers to the union of the SKOS vocabulary and the XL vocabulary.

Here "the XL data model" refers to the class and property definitions stated in this appendix only. "The SKOS+XL data model" refers to the union of the data model defined in sections 3-10 above and the XL data model.

A.2. The xl:Label Class

A.2.1. Preamble

The class xl:Label is a special class of lexical entities.

An instance of the class xl:Label is a resource and may be named with a URI.

An instance of the class xl:Label has a single literal form. This literal form is an RDF plain literal (which is a string of UNICODE characters and an optional language tag [@@REF-RDF-CONCEPTS]). The property xl:literalForm is used to give the literal form of an xl:Label.

If two instances of the class xl:Label have the same literal form, they are not necessarily the same resource.

A.2.2. Class and Property Definitions

A.2.3. Examples

The example below describes an xl:Label named with the URI <http://example.com/ns/A>, with the literal form "foo" in English.

The four examples below are each not consistent with the XL data model, because an xl:Label is described with two different literal forms.

A.2.4. Notes

A.2.4.1. Identity and Entailment

As stated above, each instance of the class xl:Label has one and only one literal form. In other words, there is a function mapping the class extension of xl:Label to the set of RDF plain literals. This function is defined by the property extension of xl:literalForm. Note especially two facts about this function.

First, the function is not injective. In other words, there is not a one-to-one mapping from instances of xl:Label to the set of RDF plain literals (in fact it is many-to-one). This means that two instances of xl:Label which have the same literal form are not necessarily the same individual. So, for example, the entailment illustrated below is not supported by the XL data model.

Second, the function is not surjective. In other words, there may be no instances of xl:Label with a literal form corresponding to a given plain literal.

A.2.4.2. Membership of Concept Schemes

The membership of an instance of xl:Label within a SKOS concept scheme can be asserted using the skos:inScheme property.

A.3. Preferred, Alternate and Hidden xl:Labels

A.3.1. Preamble

The three properties xl:prefLabel, xl:altLabel and xl:hiddenLabel are used to give the preferred, alternate and hidden labels of a resource respectively, where those labels are instances of the class xl:Label. These properties are analogous to the properties of the same local name defined in the SKOS vocabulary, and there are logical dependencies between these two sets of properties defined below.

A.3.2. Class and Property Definitions

A.3.3. Examples

The example below illustrates the use of all three XL labeling properties, and is consistent with the SKOS+XL data model.

A.3.4. Notes

A.3.4.1. "Dumbing-Down" to SKOS Lexical Labels

The property chain axioms S51-S53 support the "dumbing-down" of XL labels to vanilla SKOS lexical labels via inference. This is illustrated in the example below.

A.3.4.2. SKOS+XL Labeling Integrity

Note the two integrity conditions on the SKOS labeling properties defined in Section 5. First, the properties skos:prefLabel, skos:altLabel and skos:hiddenLabel are pairwise disjoint. Second, a resource has no more than one value of skos:prefLabel per language. Because of the property chain axioms defined above, the following four examples, whilst consistent w.r.t. the XL data model alone, are not consistent with the SKOS+XL data model.

A.4. Links Between xl:Labels

A.4.1. Preamble

This section defines a pattern for representing binary links between instances of the class xl:Label.

Note that the vocabulary defined in this section is not intended to be used directly, but rather as an extension point which can be refined for more specific labeling scenarios.

A.4.2. Class and Property Definitions

A.4.3. Examples

The example below illustrates a link between two instances of the class xl:Label.

A.4.4. Notes

A.4.4.1. Refinements of this Pattern

As mentioned above, the xl:labelRelation property serves as an extension point, which can be refined for more specific labeling scenarios.

For example, below a third party has refined the property skos:labelRelation to express acronym relationships, and used it to express the fact that "FAO" is an acronym for "Food and Agriculture Organization".

Note that a sub-property of a symmetric property is not necessarily symmetric.

Appendix B. SKOS Overview

Appendix C. SKOS Data Model as RDF Triples

The SKOS Data Model as RDF Triples can be found at http://www.w3.org/2008/05/skos.

Note that it is not possible to express all of the statements of the SKOS data model as RDF triples.

The SKOS eXtension for Labels (XL) Data Model as RDF Triples can be found at http://www.w3.org/2008/05/skos-xl.

Note also that it is not possible to express all of the statements of the XL data model as RDF triples.