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Abstract

Knowledge organization systems, such as taxonomies, thesauri or subjectheading lists, play a fundamental role in information structuring and access.The Semantic Web Deployment Working Group aims at providing a model forrepresenting such vocabularies on the Semantic Web: SKOS (Simple KnowledgeOrganization System).

This document presents the preparatory work for the 2009 version of SKOS[SKOS-REFERENCE]. It listsrepresentative use cases, which were obtained after a dedicated questionnairewas sent to a wide audience. It also features a set of fundamental orsecondary requirements derived from these use cases, that have been used toguide the design of SKOS.

This document is a companion to theSKOS Reference and theSKOS Primer, which respectivelyprovide the normative reference on SKOS and a user guide for those who wouldlike to represent their concept scheme using SKOS.

Status of this document

This section describes the status of this document at the time of itspublication. Other documents may supersede this document. A list of currentW3C publications and the latest revision of this technical report can befound in theW3C technical reports indexathttp://www.w3.org/TR/.

This document is an editorial update to the first publicWorking Draft of the "SKOS (SimpleKnowledge Organization System) Use Cases and Requirements", developed by theW3CSemantic Web Deployment WorkingGroup [SWD]. Publication of this version isconcurrent with the advancement of theSKOS Referenceto W3C Recommendation.

TheUse Cases detailed in this document have beenselected as representative of the use cases submitted in response to a "Callfor Use Cases" published in December 2006. These use cases as well asIssues identifiedby the working group have resulted in draftRequirements that will guide the design of thefuture SKOS Recommendaton. Early feedback is therefore most useful. Feedbackon use cases that can help to resolveopen issues isespecially important. Note also that any feature listed underCandidate Requirements should be considered as "atrisk" without further feedback.

Comments on this document may be sent topublic-swd-wg@w3.org; please includethe text "[SKOS] UCR comment" in the subject line. All messages received atthis address are viewable in apublic archive.

Publication as a Working Group Note does not imply endorsement by the W3C Membership. This is a draft document and may be updated, replaced or obsoleted by other documents at any time. It is inappropriate to cite this document as other than work in progress.

This document was produced by a group operating under the5 February 2004W3C Patent Policy. W3C maintains apublic list of anypatent disclosures made in connection with the deliverables of the group;that page also includes instructions for disclosing a patent. An individualwho has actual knowledge of a patent which the individual believes containsEssentialClaim(s) must disclose the information in accordance withsection6 of the W3C Patent Policy.

Table of Contents

1Introduction

Knowledge organization systems play a fundamental role in informationstructuring and access, e.g. for asset description or web site organization.Such vocabularies, coming in the form of thesauri, classification schemes,subject heading lists, taxonomies or even folksonomies, are developed andused worldwide, by institutions as well as individuals. However these veryimportant knowledge resources are still mostly isolated from the outsideworld, and not widely used in implementing systems.

The development of new information technologies and infrastructures, suchas the World Wide Web, calls for new ways to create, manage, publish and usethese knowledge organization systems. It is especially expected thatconceptual schemes will benefit from greater shareability, e.g. by beingpublished via web services. In the meantime, the documentary systems whichuse them will turn to advanced information retrieval techniques to constructmost of their semantic structure and lexical content.

SKOS (Simple Knowledge Organization System) provides a model to representand use vocabularies and ontologies in the framework of the Semantic Web. Afirst version [SWBP-SKOS-CORE-GUIDE] has beenproduced by the Semantic Web Best Practices and Deployment working group[SWBPD], and is already used in someresearch projects. The Semantic Web Deployment Working Group [SWD] has been chartered to continue this work, and to"produce guidelines and an RDF vocabulary (SKOS) for transforming an existingvocabulary representation into an RDF/OWL representation" [SWD-Charter].

In order to delimit the scope and elicit the required features for SKOS,the SWD working group has issued in December 2006 a call for use cases,asking for descriptions of existing or planned SKOS applications, accordingto a specificquestionnaire.Following the gathering of these use cases, the Working Group has elicited anumber of requirements for SKOS which are motivated by the previous work onSKOS, or by contributions received following the call for use cases.

This document gives an account of this process. First, section 2 presentssummaries of selected contributions, and pointers to the complete set ofcases which were sent to the Working Group. Second, section 3 lists therequirements the Working Group has elicited following the call for usecases.

2 Use Cases

2.1 Use Case #1 — An integrated view to medievalilluminated manuscripts

(Contributed by Antoine Isaac.
Complete description available athttp://www.w3.org/2006/07/SWD/wiki/EucManuscriptsDetailedand athttp://www.w3.org/2006/07/SWD/wiki/EucIconclassDetailed)

The purpose of this application is to provide the user with access to twocollections of illuminated manuscripts from the Dutch and French nationallibraries,Medieval Illuminated Manuscripts andMandragore(accessible online athttp://www.kb.nl/manuscripts andhttp://mandragore.bnf.fr). Thedescriptions of images from these two collections follow different metadataschemes, and contain values from different controlled vocabularies forsubject indexing. The user should however be able to search for items fromthe two collections using his preferred point of view, either usingvocabulary from collection 1 or vocabulary from collection 2.

The main feature of the current application—available on the STITCHproject site,http://stitch.cs.vu.nl/—iscollection browsing (previously in BNF_KB_demo.html), which uses hierarchical links in vocabularies: if aconcept matching a query has subconcepts, the documents indexed against thesesubconcepts should be returned. The application also uses mapping linksbetween concepts from the two vocabularies. For example, if an equivalencelink is found between a query concept from one vocabulary and another conceptfrom the second one, documents indexed by this other concept shall also beincluded in the query results.

Requires:R-ConceptualRelations,R-IndexingRelationship

Additionally, the application enables search based on free text queriesover the collection metadata: documents can be retrieved based on free-textquerying of the different fields used to describe the documents (creator,place, subject, etc.). For subject indexing, if a text query matches thelabel of a controlled vocabulary concept, the documents indexed against thisconcept will be returned.

The two collections use respectively the Iconclass and Mandragore analysisvocabularies.

Iconclass (http://www.iconclass.nl)contains 28,000 classes used to describe the subjects of an image (persons,event, abstract ideas). Complete versions are available for English, German,French, Italian, and partial translations for Finnish and Norwegian.

Requires:R-MultilingualLexicalInformation

The main building blocks of Iconclass aresubjects, used todescribe the subjects of images. An Iconclass subject consists of anotation (an alphanumeric identifier used for annotation) and atextual correlate (e.g. “25F9 mis-shapen animals; monsters”).Subjects are organized in hierarchical trees, as in the following extract:

Subjects can have associative cross-reference links between them(systematic references) and are linked tokeywords that areused to search for them in Iconclass tools. Keywords form a network of theirown, featuringsee links (from one non-preferred keyword, notattached to any subject, to a preferred one),see also links(between keywords that are semantically or iconographically related) andtranslation links (between keywords in different languages).

Requires:R-LabelRepresentation,R-RelationshipsBetweenLabels

Iconclass additionally providesauxiliary mechanisms for subjectspecialization at indexing time. These actually allow for collection-specificvocabulary extension:

• by specializing a conceptual "placeholder" into a named individual (bracketed text) :11H(…) saints can be specialized into11H(VALENTINE), which does not exist in the standard Iconclass,
• or by combining an existing subject with special auxiliaries (e.g.keys andstructural digits):25F2 mammals can be combined with(+33) head of an animal, resulting in25F(+33) which will index an image of a mammal's head. Or11H(VALENTINE)2 can be synthesized from11H(VALENTINE) and11H(...)2 early life of male saint to index an image which specifically denotes the early days of St. Valentine.

Requires:R-ConceptSchemeExtension,R-SkosSpecialization,R-IndexingAndNonIndexingConcepts,R-ConceptCoordination

Maintenance of the vocabulary is done via manual editing ofsemi-structured source files. As a general rule, the standard version willonly be changed in a conservative way, not modifying the existingsubjects.

Mandragore contains 16,000 subjects. 15,800 aredescriptors,which are used to describe the illuminations and form a flat list. Additionalstructure is given by 200abstract topic classes which form ahierarchy organizing the descriptors according to general domains, but cannotthemselves be used to describe documents:

A descriptor is specified by a French label (“cochon”, for pig),optional rejected forms (“porc”), an optional definition (“mamifèreongulé”, hoofed mammal) and a reference to one or more topic classes(“.mammifères”, mammals). A note can sometimes be found as acomplementary definition.

To enable integrated browsing, elements from Mandragore and Iconclassvocabularies must be linked together using equivalence or specializationlinks as in the following:

Requires:R-ConceptualMappingLinks

2.2 Use Case #2 — Bio-zen ontology framework forrepresenting scientific discourse in life science

(Contributed by Matthias Samwald,Medizinische Universität Wien.
Complete description available athttp://www.w3.org/2006/07/SWD/wiki/EucBiozenDetailed)

Bio-zen (http://neuroscientific.net/index.php?id=43)allows the description of biological systems and the representation ofscientific discourse on the web in a highly distributed manner. It isintended to be used by researchers and developers in the life sciences.

SKOS is used in bio-zen for the representation of many existing lifesciences vocabularies, taxonomies and ontologies coming from the "OpenBiomedical Ontologies" (OBO) collection (http://www.fruitfly.org/~cjm/obo-download/).The size of all converted taxonomies taken together is on the order ofmillions of concepts. Typical examples are the Gene Ontology or MedicalSubject Headings (MeSH), an entry of which is displayed here:

Requires:R-ConceptualRelations,R-LabelRepresentation,R-TextualDescriptionsForConcepts

To represent such vocabulary elements as well as other types ofinformation, the existing SKOS model has been integrated into a single OWLontology, together with the DOLCE foundational ontology and the Dublin Coremetadata model. In the process, the SKOS model has been extended with specialtypes of concepts, e.g. biozen:sequence-concept. To enable efficientreasoning with the available dataset, it is important to note that existingconstructs have been made compatible with the OWL-DL language.

Requires:R-CompatibilityWithOWL-DL

The bio-zen framework will consist of several applications, especiallySemantic Wikis. A Bio-zen ontology incorporates constructs to make statementsabout digital information resources, that is creating "concept tags". Thisconcept-tagging is an important feature of bio-zen, because it eases theintegration of information from different sources.

Requires:R-IndexingRelationship

2.3 Use Case #3 — Semantic search service across mappedmultilingual thesauri in the agriculture domain

(Contributed by Margherita Sini and JohannesKeizer, Food and Agriculture Organization.
Complete description available athttp://www.w3.org/2006/07/SWD/wiki/EucAimsDetailed)

This application coming from the AIMS project (http://www.fao.org/aims) is a semanticsearch service that makes use of mapped agriculture thesauri. It allows usersto search any available terminology in any of the languages in which thethesauri are provided and retrieve information from resources which may havebeen indexed by one of the mapped vocabularies. Typical functions arenavigating resources, helping to build boolean searches via conceptidentification, or expanding given searches by extra languages orsynonyms.

Requires:R-IndexingRelationship

The service builds on several agriculture vocabularies: the AgrovocThesaurus (http://www.fao.org/aims/ag_intro.htm),the Agris/Caris Classification Scheme (ASC), the FAO Technical KnowledgeClassification Scheme (TKCS), the subjects from the FAOTERM vocabulary,etc.

Agrovoc contains 35,000terms in 12 languages (not all of thelanguages feature the same translated terms, however), while ASC, TCKS andFAOTERM range between 100 and 200 categories available in the 5 official FAOlanguages. Agrovoc terms consist of one or more words and always represent asingle concept. Terms are divided intodescriptors andnon-descriptors, the first currently only used for indexing. Foreach descriptor, a word block is displayed showing the relation to otherterms: BT (broader term), NT (narrower term), RT (related term), UF(non-descriptor). There are also scope notes, used to clarify the meaning ofboth descriptors and non-descriptors.

Requires:R-ConceptualRelations,R-LabelRepresentation,R-TextualDescriptionsForConcepts,R-MultilingualLexicalInformation

Actually, the AIMS project includes some more specific links, presented inhttp://www.fao.org/aims/ (formerly in cs_relationships.htm:Concept-to-Concept relationships (subclass of; caused by; member of; partof), Term-to-Term relationships (related term; synonym; translation) andString-to-String relationships (spelling variant; acronym).

Examples of such links are:

Requires:R-SkosSpecialization,R-RelationshipsBetweenLabels

Currently the Agrovoc management system lacks distributed maintenance, butit is expected that a new system will soon solve this problem, which iscrucial since changes are made by experts from all over the world.

For AIMS, Agrovoc has been converted into SKOS and is being mapped to twoother vocabularies: the Chinese Agricultural Thesaurus (CAT) and the NationalAgricultural Library thesaurus (NAL). This mapping uses links inspired by theSKOS mapping vocabulary [SWBP-SKOS-MAPPING], as below:

Requires:R-ConceptualMappingLinks

2.4 Use Case #4 — Supporting productlife cycle

(Contributed by Sean Barker, BAE Systems.
Complete description available athttp://www.w3.org/2006/07/SWD/wiki/EucProductLifeCycleSupportDetailed)

The problem of the Product Life Cycle Support (PLCS) application is tointegrate a network of interconnected supply chains, with multiple, largecustomers buying a wide range of products (from shoes to aircraft) eachdictating their own standards, and with every supplier being part of multiplesupply chains. Each customer wants to maintain a common approach over all itssupply chains. And each supplier wants to maintain the same system for eachof the supply chains it works in.

The aim of this application is to propose a data exchange mechanism formanaging the life support of complex products (http://www.oasis-open.org), includingconfiguration definition, maintenance definition, maintenance planning andscheduling, and maintenance and usage recording (including configurationchange).

For that, an upper ontology of several hundred items for the descriptionof the product life cycle will be defined. There is no chance of the entiresupply system (10,000's of businesses) developing a single detailed model.However, given the upper ontology, they will be free to specialize individualontology terms (playing the role of place holders for local extension) tomeet their precise needs.

PLCS is conceptually a co-operatively developed web in XML, with the liveversion being a set of runtime views assembled from files submitted by adozen or so contributors. It may be useful, where ontologies diverge, to mapterms between the diverging branches, either to indicate where terms can beharmonized to their equivalent, or to identify that there is a similaritylink that is not exact equivalence.

Requires:R-ConceptualRelations,R-ConceptSchemeExtension,R-ConceptualMappingLinks

The PLCS vocabulary addresses hundreds of separate functions, includingclassification of items, classification of information usages (e.g. types ofpart identifier), classification of entity roles (e.g. date as start date) orclassification of relationships (e.g. supersedes).

Typical examples of terms are:

The vocabulary has been encoded using OWL, and is managed via the ProtegeOWL editor.

Requires:R-TextualDescriptionsForConcepts

2.5 Use Case #5 — CHOICE@CATCH ranking ofcandidate terms for description of radio and TV programs

(Contributed by Véronique Malaisé andHennie Brugman, Vrije Universiteit Amsterdam and Max Planck Institute forPsycholinguistics.
Complete description available athttp://www.w3.org/2006/07/SWD/wiki/EucRankingForDescriptionDetailedand athttp://www.w3.org/2006/07/SWD/wiki/EucGtaaBrowser)

Radio and television programs at the Dutch national broadcasting archive(Sound and Vision,http://www.beeldengeluid.nl) aretypically associated with contextual text descriptions: web site texts,subtitles, program guide texts, texts from the production process, etc. Thesecontext documents are used by documentalists at Sound and Vision who manuallydescribe programs using concepts from the GTAA thesaurus (GemeenschappelijkeThesaurus Audiovisuele Archieven—Common Thesaurus for AudiovisualArchives).

The CHOICE project (part of the Dutch CATCH research program) uses naturallanguage processing techniques to automatically extract candidate GTAA termsfrom the context documents. The application focused on in this section takesthese candidate terms as input, and ranks them on the basis of the structureof the GTAA thesaurus. For example, the fact that "Voting" and"Democratization" are related in GTAA by a two-step path (via the "Election"term and two "related-to" links) will positively influence the ranking ofthese terms. Ranked terms will be presented to documentalists to speed uptheir description work.

The GTAA vocabulary covers a wide range of topics, as it is meant todescribe anything that can be broadcast on TV or radio. It containsapproximately 160,000 terms, divided into 6 disjoint facets: Keywords,Locations, Person Names, Organization-Group-Other Names, Maker Names, andGenres.

The thesaurus mainly uses constructs from the ISO 2788 standard, likeBroader Term, Narrower Term, Related Term and Scope Notes. Terms from allfacets of the GTAA may have Related Terms, Use/Use For and Scope Notes, butonly Keywords and Genres can also have Broader Term/Narrower Term relations,organizing them into a set of hierarchies. In addition to these standardfeatures, Keywords terms are thematically classified in 88 subcategories of16 top Categories.

Requires:R-ConceptualRelations,R-LabelRepresentation,R-SkosSpecialization

The application, envisioned as a SOAP web service, uses a Sesame RDF webrepository (http://openrdf.org) containingthe SKOS version of the GTAA thesaurus to retrieve the 'term contexts' of theterms in the input list, which is stored in a local RDF repository.

This term context includes, for one given term, all terms that aredirectly connected to it by Broader Term, Narrower Term or Related Termrelations. This includes pre-computed inter-facet links that are not part ofthe ISO standard, though allowed by the GTAA data model. For example, one canlink a "King" in the Person facet to the general subject "Kings" and thecountry which this King rules.

For the ranking, it is now assumed that candidate terms that are mutuallyconnected by thesaurus relations (directly or indirectly) are more likely tobe good descriptions than isolated candidate terms. Later on, it might beinteresting to differentiate between types of thesaurus relations, or to usemore complex patterns of these relations.

The thesaurus-based recommendation system can also be integrated with arecommendation system that is based on co-occurences between terms that areused in previously existing descriptions of programs.

2.6 Use Case #6 — BIRNLex: a lexicon forneurosciences

(Contributed by William Bug, DrexelUniversity College of Medicine.
Complete description available athttp://www.w3.org/2006/07/SWD/wiki/EucBirnLexDetailed)

BIRNLex is an integrated ontology+lexicon used for various purposes —some end-user/interactive, others back-end/infrastructure — within the BIRNProject to support semantically-formal data annotation, semantic dataintegration, and semantically-driven, federated query resolution.

Requires:R-ConceptualMappingLinks,R-IndexingRelationship,R-LexicalMappingLinks

Below are examples of BIRNLex class definitions that illustrate the needfor lexical support and links to external knowledge sources. The generaldesign goals have been to use both the Dublin Core metadata elements and SKOSwhere ever possible. The goal is to use SKOS for all lexical qualities. Thereare certain annotation properties that should be shared across all biomedicalknowledge resources. There are other required elements specific to thespecific needs in BIRN (the group producing BIRNLex).

Requires:R-CompatibilityWithDC,R-CompatibilityWithOWL-DL,R-ConceptualRelations,R-LabelRepresentation,R-ConceptSchemeExtension

The following is a subset of BIRNLex applications, either extant or in theoffing:

• online curation: tools to enable curating the BIRNLex ontology+lexicon via the web;
• annotation: applications designed to support domain experts annotating neuroimaging data;
• query/mediation: a mediator program federates more than 60 data repositories. Site databases register with the mediator by mapping the relevant elements from their resident data model into the mediator's global model.

In all of these applications, it is critical to have a clear, distinct,and shared representation for the associated lexicon. For instance, whenintegrating BIRN segmented brain images with those from other projects acrossthe net, use of lexical variants from a variety of public terminologies andthesauri such as SNOMED and MeSH can provide a powerful means to largelyautomate semantic integration of like entities - e.g., corresponding brainregion, equivalent behavioral assays described using different preferredlabels/names. In providing a community shared formalism for representing theassociated lexicon, SKOS can greatly simplify this task. If, for instance,the lexical repository (collection of Lexical Unique Identifier, each lexicalvariant of a term getting one LUI) contained in UMLS were representedaccording to SKOS, this would provide an extremely valuable resource to thecommunity of semantically-oriented bioinformatics researchers, as well as apowerful tool to support latent semantic analysis or natural languageprocessing when linking to unstructured text.

The following are the collection of terminologies and ontologies beinglinked into BIRNLex: Neuronames, Brainmap.org classification schemes,RadLex, Gene Ontology, Reactome, OBI, PATO, SubcellularAnatomy Ontology (CCDB -http://ccdb.ucsd.edu/), MeSH.

Neuronames concerns brain anatomy and is about 750 classes and thousandsof associated lexical variants. Brainmap.org classification includeshierarchies to describe neuroanatomy, subject variables, stimulus conditions,and experimental paradigms associated with functional MRI of the nervoussystem The Subcellular Anatomy Ontology is designed to describe thesubcellular entities associated with ultrastructural and histological imagingof neural tissue. Currently the application is only dealing with Englishlexical entries.

BIRNLex curators are working with the National Center for BiomedicalOntology (NCBO) to adopt the OBO Foundry recommendations in the constructionof BIRNLex. Use of SKOS elements can be useful, so that, for instance,software applications can draw on "skos:prefLabel", "obo_annot:synonym","obo_annot:definition", etc.

The management of BIRNLex is currently done manually in Protege-OWL.

Requires:R-CompatibilityWithOWL-DL

However, the ultimate goal is to adopt a client-server infrastructure thatwill create an RDF-based backend store and support both curation of theontology and annotation using the ontology via Java Portlet-basedapplications. BIRN has a core infrastructure staff dedicated to use of theGridSphere Java Portlet implementation framework (www.gridsphere.org).

2.7 Use Case #7 — Radlex: a lexicon for radiology

(contributed by Curt Langlotz.
Complete description available athttp://www.w3.org/2006/07/SWD/wiki/EucRadlexDetailed)

RadLex provides a structured vocabulary of terms used in the field ofradiology. Currently completed are listings of anatomic terms and "findings",which includes things that can be seen on or inferred from images produced byradiologists. These two sets include a total of about 7,500 terms. A list ofthe terms used to describe the creation of such images, including informationabout the equipment used and the various imaging sequences performed, will becomplete by the end of 2007.

An example application demonstrating functionality is an image annotationprogram that reads in RadLex and provides users the ability to search for anduse particular RadLex terms to associate with images, post-coordinating themif necessary. Users would want to be able to retrieve RadLex terms by name orsynonym.

Requires:R-ConceptualRelations,R-LabelRepresentation,R-TextualDescriptionsForConcepts,R-ConceptCoordination

RadLex, which can be searched and browsed online atwww.radlex.org, is a taxonomy currentlybuilt predominantly using is-a relations. But there are also part-of andother relations (especially for anatomy), and new relations will be added asRadLex expands. Each term has a rich set of metadata fields to includeprovenance information and terminological data such as synonyms, definition,and related terms from other vocabularies.

The practical fields include:

• Term name
• ID number (with no inherent semantics)
• parents, and their relation to the term
• children, and their relation to the term

and optionally, any

• mappings to other vocabularies
• definition
• synonyms
• source (a reference publication which includes this term)
• other comments (such as derivation of the term, special or preferred uses of it, etc.)

Requires:R-ConceptualRelations,R-AnnotationOnLabel,R-RelationshipsBetweenLabels,R-LexicalMappingLinks

The relationships used among terms include:

• Continuous with [DEF: Two structures are “continuous with” one another if they are immediately adjacent and physically connected to one another. This relationship is often used for cavitary and tubular structures, such as the continuity between the left ventricle and the aorta. This is a reflexive relation.]
• Branch of [DEF: a smaller conduit is a “branch of” a larger conduit if it is one of a group of two or more conduits that continue in the direction of flow where only one conduits had existed before. It is permissible for one of the continuing conduits to maintain the same name of original conduits. This relation is often used with arteries.]
• Branch [DEF: the converse relation to “branch of”]
• Tributary of [DEF: a smaller conduit is a “tributary of” a larger conduit if it is one of a group of two or more vessels that join to form a single conduit in the direction of flow. This relation is often used with veins. ]
• Tributary [DEF: the converse relation to “tributary of”]
• Part of [DEF: one object is “part of” another object if it comprises less than all of the other object. This relation is often used with solid body parts and organs.]
• Segment of [DEF: one tubular structure is a “segment of” another if it defines a part of that structure divided perpendicular to the axis of the tube. This relation is often used to define the subparts of tubular structures, such as arteries, veins, and intestines.
• Part [DEF: the converse relation to “part of”]
• Segment [DEF: the converse relation to “segment of”]
• Contained_in [DEF: One structure is “contained in” another if the first structure is inside the other. For example the liver is contained in the abdominal cavity.]
• Contains [DEF: the converse relation to “contained in”]
• Is_a; Type of
• Member of [DEF: One structure is a “member of” a set of structures. For example, the “liver” is a member of the “set of viscera of abdomen”]
• Member [DEF: the converse relation to “member of”]
• Synonym [DEF: the term is a less-preferred synonym of a preferred term]

For instance, “nervous system” has a part called “brain”, and“nervous system” contains “nervous system spaces”. The view of thehierarchy itself does not reveal the relationships among the terms; thisinformation is found within the term features, shown in this format on theright-hand side. In this framework, the hierarchy is generated from thedifferent relationships among terms, using either SPARQL or a custominterface to an application that consumes the terminology.

There are 9 separate hierarchies in the vocabulary: Treatment; Imageacquisition, Processing and Display; Modifier; Finding; Anatomic Location;Uncertainty (to be renamed Certainty); Teaching Attribute; Relationship; andImage Quality. There are currently no relations holding between terms indifferent hierarchies, though this could be developed in future (e.g. linkingof particular Findings to potential Anatomic Locations).

The Radlex vocabulary is provided in English, with plans to include otherlanguages (e.g., German).

Requires:R-MultilingualLexicalInformation

Protégé has been used to create a machine-readable version of thevocabulary, which is available athttp://www.rsna.org/radlex/downloads.cfm.RadLex will be available in OWL-DL in the future.

Requires:R-CompatibilityWithOWL-DL

During the design of the vocabulary, basic guidelines from Cimino andChute were used, such as ensuring that a term only corresponds to oneconcept. As the terminology is being developed into a more structured form,with more types of relationships, different parents are being allowed as longas the relationship type is different. E.g. one IS-A parent, one PART-OFparent, etc.

Potential changes in the vocabulary are submitted to the chair of theRadLex Steering Committee of the Radiological Society of North America, whoconsults with the relevant lexicon development committee. Accepted changesare periodically incorporated into the vocabulary. The first release was madepublic in November 2006.

Currently, a mapping is being developed between RadLex and thecorresponding terms/codes in SNOMED (Systematized Nomenclature of Medicine)and the ACR (American College of Radiology) Index, the vocabularies that wereused as a starting point for terminology development.

From a representational point of view, this mapping shall consist ofequivalence and specialization links. Later, we expect people to composeatomic terms (post-coordination) to describe composite entities.

Requires:R-ConceptCoordination

2.8 Use Case #8 — NSDL Metadata Registry

(Contributed by Jon Phipps, CornellUniversity.
Complete description available athttp://www.w3.org/2006/07/SWD/wiki/RucMetadataRegistryExtended)

The NSDL Registry is intended to provide a complete vocabulary developmentand management environment for development of controlled vocabularies.Services are primarily directed at vocabulary owners and include provisionsfor:

• managing access and editing rights for groups of vocabulary maintainers maintaining individual vocabularies
• import and management of existing vocabularies, with and without existing URIs
• namespace management and maintenance services providing permanent URIs
• registered users to receive notifications of changes to vocabularies to which they have subscribed
• content negotiation for retrieval of registered vocabularies in various formats, currently RDF/XML (rdf), XHTML (html), and XML Schema (xsd)
• content negotiation and resolution services for registered vocabularies in non-registry namespaces (in alpha)
• controlled concept editing and maintenance using SKOS properties
• controlled editing of reciprocal relationships between concepts (Requires: SKOS status property in order to manage reciprocal endorsement)
• controlled mapping of relationships between concepts in different vocabularies. Seehttp://www.w3.org/2006/07/SWD/wiki/RucMetadataRegistryExtended. (Requires:R-ConceptualMappingLinks)
• concept-level change history management (Requires: URI content negotiation and http 301/302 redirection)
• vocabulary- and concept-level version management (in alpha)
• multilingual vocabulary maintenance (Requires: the ability to manage concept equivalence between vocabularies in different languages and intra-concept language-related equivalence between concept properties in multiple languages)
• skos validation by user input constraint and validation of imported vocabularies (Requires:R-ConsistencyChecking)
• search and browse for concepts by label

The registry currently has a number of vocabularies registered. A sampleentry of a vocabulary/scheme and a single concept are shown below (taken fromhttp://metadataregistry.org/uri/NSDLEdLvl.html):

2.9 Other use cases

The SWD Working Group maintains on its wiki site the complete list ofdescriptions that were received following its call for use cases:

• Geographical web service for hierarchical browsing (contributed by Walter Koch).
  Summed up description available athttp://www.w3.org/2006/07/SWD/wiki/EucTgn.
• Representation of Tactical Situation Objects (contributed by Sean Barker).
  Summed up description available athttp://www.w3.org/2006/07/SWD/wiki/EucTacticalSituationObject.
• GTAA Web Browser (contributed by Véronique Malaisé and Hennie Brugman).
  Summed up description available athttp://www.w3.org/2006/07/SWD/wiki/EucGtaaBrowser.
• Ontology for Biomedical Investigation (OBI) for for describing methods in biomedical research (contributed by Trish Whetzel).
  Detailed description available athttp://www.w3.org/2006/07/SWD/wiki/EucObiDetailed.
• The STAR:dust conceptual model (contributed by Irene Celino, Emanuele Della Valle and Francesco Corcoglioniti).
  Summed up description available athttp://www.w3.org/2006/07/SWD/wiki/EucStarDust.
• UDC: the Universal Decimal Classification (contributed by Antoine Isaac and Aida Slavic).
  Description for motivatingR-ConceptCoordination available athttp://www.w3.org/2006/07/SWD/wiki/EucUDC.
• the RAMEAU subject heading language (contributed by Antoine Isaac).
  Description for motivatingR-ConceptCoordination available athttp://www.w3.org/2006/07/SWD/wiki/EucRameau.
• XMDR: Extended Metadata Registry Prototype (contributed by John McCarthy).
  Detailed description available athttp://www.w3.org/2006/07/SWD/wiki/EucXmdrDetailed.
• High-level Thesaurus (HILT) Project (contributed by George Macgregor).
  Detailed description available athttp://www.w3.org/2006/07/SWD/wiki/EucHiltDetailed.
• A Semantic Grid Browser for the Life Sciences Applied to the study of Infectious Diseases (contributed by Simon Jupp).
  Detailed description available athttp://www.w3.org/2006/07/SWD/wiki/EucSeaLifeDetailed.
• Semantic-based Framework for Personalized TV Content Management in a cross-media environment (contributed by Pieter Bellekens).
  Detailed description available athttp://www.w3.org/2006/07/SWD/wiki/EucPersonalizedTvDetailed
• intraLibrary: Support for web-based Repository of Learning Objects (contributed by Sarah Currier).
  Detailed description available athttp://www.w3.org/2006/07/SWD/wiki/EucIntraLibraryDetailed.
• Hybrid and Network-Assisted Vocabulary Interface (HANAVI) and Japan National Diet Library List of Subject Headings (NDLSH) (contributed by Mitsuharu Nagamori).
  Detailed description available athttp://www.w3.org/2006/07/SWD/wiki/EucHanaviDetailed.
• Squiggle: an application framework for model-driven development of real-world Semantic Search Engines (contributed by Irene Celino).
  Detailed description available athttp://www.w3.org/2006/07/SWD/wiki/EucSquiggleDetailed.
• Conceptual Open Hypermedia Service (COHSE) (contributed by Sean Bechhofer).
  Detailed description available athttp://www.w3.org/2006/07/SWD/wiki/EucCohseDetailed.
• The Semantic Web Environmental Directory (SWED) (contributed by Alistair Miles).
  Detailed description available athttp://www.w3.org/2006/07/SWD/wiki/EucSwedDetailed.

3 Requirements

The use cases presented in the previous section motivate a number ofrequirements that the SKOS specification must or should meet in order tofulfill its aim as a standard model for porting simple concept schemes on thesemantic web. Depending on the level of consensus reached in the WorkingGroup, these requirements were categorized intoaccepted andcandidate requirements.

This document reflects decisions that were made just after having receivedthe use cases, at the beginning of 2007. The requirements were subsequentlyexamined asissues by the Working Group, which decided on actions totake—or not—in order to close them. The final status of the issuesmentioned in this document is accessible via theSWD Issue Tracker,athttp://www.w3.org/2006/07/SWD/track/issues.

Note: in the following, to avoid ambiguities,vocabulary will beused to refer to theSKOS vocabulary, that is, the set of constructs(classes, properties) introduced in the SKOS model.Concept Schemewill be used to refer to the objects built with SKOS, i.e. theapplication-specific collections of concepts that are mentioned in SKOS usecases.

3.1 Accepted requirements

R-ConceptualRelations

Representation of relationships between concepts

The SKOS model shall provide semantic relationships between concepts, for display or search purposes. Typical examples are the hierarchical relationsbroader than (BT),narrower than (NT) and the non-hierarchical associative relationrelated to (RT).

Motivation:Tgn,Manuscripts,Aims,ProductLifeCycleSupport,RankingForDescription, etc.

R-ConceptSchemeExtension

Extension of concept schemes

A concept scheme might be locally extended with new concepts referring to existing ones, e.g. as specializations of these.

Motivation:Manuscripts,BirnLex,ProductLifeCycleSupport

R-ConceptualMappingLinks

Correspondence/Mapping links between concepts from different concept schemes

In order to build links between concepts coming from different concept schemes, SKOS should provide proper semantic relationships. Possible links, similarly to the ones found existing SKOS and SKOS mapping [SWBP-SKOS-MAPPING] vocabularies, include concept equivalence and specialization/generalization relations.

Motivation:Manuscripts,Aims,ProductLifeCycleSupport,BirnLex,MetadataRegistry

Corresponding issues:39,71

R-LabelRepresentation

Representation of basic lexical values (labels) associated to concepts

The SKOS model shall provide means to represent the labels (preferred or not) of a concept, for display or search purposes.

Motivation:Tgn,Manuscripts,Aims,RankingForDescription, etc.

R-MultilingualLexicalInformation

Representation of lexical information in multiple natural languages

The lexical information specified in concept schemes (labels, but also definitions and notes) could come in different natural languages. A typical example is the case of a multilingual concept scheme with concepts having labels translated in several languages.

Motivation:Manuscripts ,Aims,RadLex

R-SkosSpecialization

Local specialization of SKOS vocabulary

For particular situations, the designer of a SKOS concept scheme should be able to introduce new model-level classes and properties, and link them to existing SKOS constructs. Possible cases include the creation of specific kinds of textual definitions or notes for concepts, or the specification of new types of concepts.

Motivation:Manuscripts,Tgn,Aims,Biozen,RankingForDescription

Corresponding issue:37

R-TextualDescriptionsForConcepts

Representation of textual descriptions attached to concepts

The SKOS model shall provide means to represent descriptive notes that could help understanding the elements of concept schemes, e.g. scope notes explaining the way concepts are used to describe documents.

Motivation:Aims,ProductLifeCycleSupport,TacticalSituationObject,BirnLexDetailed, etc.

Corresponding issue:64

3.2 Candidate requirements

R-AnnotationOnLabel

Ability to represent annotations on lexical items

Labels, which are currently modeled as literals in SKOS, as well as possibly other literals, are valid subjects of discourse when modeling concept schemes, e.g. when recording the dates during which a particular label was in common use. However, in RDF only resources may be subjects of statements, and literals may only be objects of statements. The question then arises, how are we to annotate labels and other literals, that is to relate them as subjects, to other entities.

Motivation:RadLex

Corresponding issue:27

R-CompatibilityWithDC

Compatibility between SKOS and Dublin Core Abstract Model

Using SKOS model shall be compatible with using Dublin Core Abstract Model [DCAM]. When there are links between SKOS features and Dublin Core ones, these shall be specified.

Motivation:BirnLex

Corresponding issue:50

R-CompatibilityWithISO11179

Compatibility between SKOS and ISO11179[Part 3]

SKOS model shall be compatible with part 3 of ISO 11179 specifications [ISO11179-3].

Corresponding issue:51

R-CompatibilityWithISO2788

Compatibility between SKOS and ISO2788

SKOS model shall be compatible with ISO 2788 specifications [ISO2788].

Corresponding issue:52

R-CompatibilityWithISO5964

Compatibility between SKOS and ISO5964

SKOS model shall be compatible with ISO 5964 specifications [ISO5964].

Corresponding issue:53

R-CompatibilityWithOWL-DL

OWL-DL compatibility

SKOS should provide a legal OWL-DL ontology, to be compatible with most common editors and reasoners.

Motivation:Biozen,BirnLex,RadLex

Corresponding issue:38

R-ConceptCoordination

Coordination of concepts

SKOS should provide the ability to create new concepts from existing ones, e.g. by using special qualifiers that add a shade of meaning to a normal concept.

Motivation:Manuscripts,RadLex,UDC,Rameau

Corresponding issue:40

R-ConceptSchemeContainment

Ability to explicitly represent the containment of any SKOS individual or statement within a concept scheme

It shall be possible to explicitly represent the containment of any individual which is an instance of a SKOS class (e.g. skos:Concept) or statement that uses SKOS property as predicate (e.g. skos:broader) within a concept scheme.

Corresponding issue:36

R-ConsistencyChecking

Checking the consistency of a concept scheme

Some SKOS applications might require testing the integrity of their concept scheme data. For example, conceptual relationships should only apply to individuals of type skos:Concept, and not for example between the (non-preferred) labels of concepts.

Motivation:GtaaBrowser,MetadataRegistry

Corresponding issue:35

R-GroupingInConceptHierarchies

Ability to include grouping constructs in concept hierarchies in thesauri

Concept schemes can contain elements (arrays,guide terms, etc.) used to group normal concepts together, e.g. based on a shared semantic property. While these special elements cannot be used for description purposes, they can be introduced in a concept scheme's hierarchy by means of generalization and specialization links.

Corresponding issue:33

R-IndexingAndNonIndexingConcepts

Ability to distinguish between concepts to be used for indexing and for non-indexing

SKOS should provide different classes for conceptual entities that can be used for indexing resources and for those that cannot be used for such a purpose (e.g. specific qualifiers that can only be used to narrow down the meaning of an existing concept).

Motivation:Manuscripts,UDC,Rameau

Corresponding issue:46

R-IndexingRelationship

Ability to represent the indexing relationship between a resource and a concept that indexes it

The SKOS model should contain mechanisms to attach a given resource (e.g. corresponding to a document) to a concept the resource is about, e.g. to query for the resources described by a given concept.

Motivation:Manuscripts,Biozen,Aims,BirnLex

Corresponding issue:48

R-LexicalMappingLinks

Correspondence mapping links between lexical labels of concepts in different concept schemes

In the process of mapping different concept schemes, it should be possible to identify correspondence links not only between concepts from these concept schemes, but also between the labels that can be attached to these concepts.

Motivation:RadLex,BirnLex

Corresponding issue:49

R-MappingProvenanceInformation

Ability to record provenance information on mappings between concepts in different concept schemes

It shall be possible to record provenance information on mappings between concepts in different concept schemes.

Corresponding issue:47

Motivation:MetadataRegistry

R-RelationshipsBetweenLabels

Representation of links between labels associated to concepts

The SKOS model shall provide means to represent relationships between the terms associated with concepts. Typical examples are translation links between labels from different languages, or the link between one label and its abbreviation, when this stands for an alternative label for the concept.

Motivation:Manuscripts,Aims,RadLex

Corresponding issue:26

4 Conclusion

To elicit the requirements that a new version of the Simple KnowledgeOrganization System (SKOS) should meet, the Semantic Web and DeploymentWorking Group has issued a call for use cases to the different communitiesthat are concerned by the use of SKOS.

More than 25 submissions have been sent to the working group, whichillustrates the variety of usages one can make of such a proposal. In thisdocument, eight of them were selected as being the most representative.

Some of these use cases have come with very high-quality descriptions, andmost correspond to development efforts that are presently being carried out,going therefore beyond pure research hypotheses. This has given a sound basisfor the process of gathering requirements for SKOS, which the second part ofthis document describes.

Requirements were divided into accepted and candidate requirements,reflecting the level of consensus they had reached in the Working Group atthe time this document was first created (16 May 2007). In the followingmonths, the Working Group had to make a final decision regarding thecandidate requirements, either accepting them or rejecting them. It of coursehad then to adapt the existing SKOS material so that it could meet theaccepted requirements.

References

[DCAM]

DCMI Abstract Model, A. Powell, M. Nilsson, A. Naeve, P. Johnston, 7 March 2005.

[ISO11179-3]

ISO/IEC 11179-3: 2003(E), Information Technology – Metadata Registries (MDR) – Part 3: Registry metamodel and basic attributes, Second edition. R. Gates, Editor, 15 February 2003.

[ISO2788]

ISO 2788:1986 Documentation - Guidelines for the establishment and development of monolingual thesauri. Second edition. ISO TC 46/SC 9, 1986.

[ISO5964]

ISO 5964:1985 Documentation - Guidelines for the establishment and development of multilingual thesauri. First edition. ISO TC 46/SC 9, 1985.

[SKOS-REFERENCE]

SKOS Reference, Alistair Miles, Sean Bechhofer, Editors, W3C Recommendation, 18 August 2009.Latest version available at http://www.w3.org/TR/skos-reference .

[SWBP-SKOS-CORE-GUIDE]

SKOS Core Guide, A. Miles, D. Brickley, Editors, W3C Working Draft (superseded), 2 November 2005. Available at http://www.w3.org/TR/2005/WD-swbp-skos-core-guide-20051102.

[SWBP-SKOS-CORE-SPEC]

SKOS Core Vocabulary Specification, A. Miles, D. Brickley, Editors, W3C Working Draft (superseded), 2 November 2005. Available at http://www.w3.org/TR/2005/WD-swbp-skos-core-spec-20051102.

[SWBP-SKOS-MAPPING]

SKOS Mapping Vocabulary Specification, A. Miles, D. Brickley, Editors, W3C Working Draft (superseded), 11 November 2004. Available at http://www.w3.org/2004/02/skos/mapping/spec/2004-11-11.html.

[SWBPD]

The Semantic Web Best Practices and Deployment Working Group, http://www.w3.org/2001/sw/BestPractices/.

[SWD]

The Semantic Web Deployment Working Group, http://www.w3.org/2006/07/SWD/.

[SWD-Charter]

Semantic Web Deployment Working Group (SWDWG) Charter, http://www.w3.org/2006/07/swdwg-charter.

Acknowledgments

The editors gratefully acknowledge contributions from Lora Aroyo, HughBarnes, Bruce Bargmeyer, Sean Barker, Sean Bechhofer, Pieter Bellekens,Hennie Brugman, Dario Cerizza, Irene Celino, Thierry Cloarec, FrancescoCorcoglioniti, Sarah Currier, Emanuele Della Valle, Diane Hillmann, ChrisHolmes, Bernard Horan, Julian Johnson, Simon Jupp, Johannes Keizer, WalterKoch, Véronique Malaisé, George Macgregor, Frédéric Martin, JohnMcCarthy, Emma McCulloch, Alistair Miles, Mitsuharu Nagamori, DennisNicholson, Matthias Samwald, Margherita Sini, Aida Slavic, DavideSommacampagna, Robert Stevens, Doug Tudhope, Andrea Turati, Bernard Vatant,Anna Veronesi.