Thesauri and the Semantic Web

Brussels, 16/12/2009 1

eContentplus

Roxanne WynsRoyal Museums of Art and History

Thesauri and the Semantic Web

Brussels16th of December 2009

Brussels, 16/12/2009 2Brussels, 16/12/2009 2

The digitalisation of cultural heritage collections is a priority task these days. It has become an important part of the core business of collection management and helps to achieve the primary and secondary goals of a cultural institution: - To register its collections (inventory)- To collect and provide scientific and documentary information on its collection - To provide access to its collections for the scientific research and for the general public

More and more institutions provide access to their growing digital collections in an online environment:- Through their own web portal - Through national partnership portals (E.g.: Vlaamse Kunstcollectie, ErfgoedPlus)- Through the EUROPEANA portal

Thesauri and the Semantic WebThesauri and the Semantic WebThesauri and the Semantic WebThesauri and the Semantic Web


Does this mean that someone interested in these digital collections can now find all information easily on the World Wide Web??

Problems- A search for information on the web often requires some knowledge on the subject and an interpretation on the search results to get to more results - A full text search does not take into account different spellings, synonyms, etc…- Sometimes it is impossible to know which term an author used to describe the object(s) you are searching for, or even whether he has used a term in the same meaning as his colleague - But the biggest problem when searching for meaningful result on the Web might be the multilingual world we live in…



When you take all of these problems into account, it becomes almost impossible to find meaningful, correct or complete results on your search



Perhaps a better example

When you search on Google for• Painter Domenikos Theotocopoulos = “El Greco” (nickname)• Some indexers use “El Greco”, others “D. Theotocopoulos”• Searching for “El Greco” does not give all results



Solution

Providing semantic relations between concepts with different lexical labels



The Semantic Web: The solution for sharing and retrieving relevant data on the Web

� Searching information often requires to combine data on the Web (e.g. searches in different digital libraries)

� Humans see the context of the data and are able to combine information easily, even if different terminologies are used

� However: machines are ignorant- partial information is unusable- difficult to make sense from, e.g., an image- difficult to combine information

Only if we formulate the conceptual meaning of the data in such way, a machine is able to read and interpret it.



So to support exchange of data on the web, we need a simple language for expressing information in machine-understandable wayTo combine different datasets:- of different origin somewhere on the web- of different formats (mysql, excel sheet, XHTML, etc)- with different names for relations (e.g., multilingual)

The principle of the semantic web is the use of ontologies. An ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts. It is used to reason about the properties of that domain, and may be used to define the domain.

An ontology aims to capture consensual knowledge, to reuse and share across software applications and by groups of people.



The W3C World Wide Web Consortium provides technologies to make data integration possibleIn short:The Semantic Web “layer cake"”Semantic Web is ...a metadata based infrastructure forreasoning on the Weban extension, not a replacement of thecurrent web

Metadata“ machine understandable” informationshared vocabularies (ontologies)a shared data model

Technological standardsRDF, OWL, SKOS,……just a technical aspect



A real Semantic Web like the so called Linking Open Data-cloud (LOD –http://linkeddata.org/) where all data on the web would we linked with each other is still far away.



But on a smaller scale, there are some interesting examples which show the possibility of the semantic web technologies to enrich cultural heritage data

Semantics in Europeana v1.0Semantics in Europeana v1.0Semantics in Europeana v1.0Semantics in Europeana v1.0

Europeana Thought lab = Task of EuropeanaConnect Work Package 1 Europeana Thought lab = Task of EuropeanaConnect Work Package 1 Europeana Thought lab = Task of EuropeanaConnect Work Package 1 Europeana Thought lab = Task of EuropeanaConnect Work Package 1 & 2 & 2 & 2 & 2 Goals:- Making Europeana a network of interoperating and aggregated surrogates that enables semantics based objects discovery and use

- Make Europeana talk European:• Multilingual search and multilingual browsing• Core language set: English, French, German, Italian, Spanish• Secondary language set: Dutch, Hungarian, Polish, Portugese, Swedish

Europeana Thought lab online: Europeana Thought lab online: Europeana Thought lab online: Europeana Thought lab online: http://europeana.eu/portal/thought-lab.htmlContains data of: Rijksmuseum Amsterdam, Musée du Louvre, Rijksbureau voor Kunsthistorische Documentatie



Europeana Thought lab online: http://europeana.eu/portal/thought-lab.html



Semantic auto-completion



Clustering of results



Matching concepts’ labels



A concept more specific than Egypte



Following other relations - creator



Following other relations – creator death place



Enabling technologies (developed by the W3C) to achieve this semantic operability are:• RDF

RDF is a universal language to describe the characteristics of resource on the web using a Subject-Predicate-Object structure (s-p-o triples). RDF triples provides a labelled connection between resources using URI-s to make it possible to link (via properties) data with one another.

An example of a “subject", "predicate", "object“ s-p-o triples:

Subject Predicate ObjectLeonardo authorOf GiocondaCimabue masterOf Giotto

In this way a machine is able to find the semantic relations between data. As a result, new relations can be found and retrieved when searching a semantic web database.



• OWL (Web Ontology Language) provides a more expressive language to enhance the exchange of informationAn example in OWL:The statementThe painting of the Sistine Chapel was carried out by Michelangelo BuonarrotiAbstracting from the statementThe painting of the Sistine Chapel (the subject) is an (instance of) activitycarried out by is a predicateMichelangelo Buonarroti is an (instance of) PersonIn OWL (conceptually)the paintingOfSistineChapel (E7.Activity) was carried_out_by (P14F) MichelangeloBuonarroti (E21.Person)In OWL (graphically)paintingOfSistineChapelcarried_out_byMichelangeloBuonarroti

But for the semantical representation of taxonomies, thesauri and conceptual schema’s, a simpler formel language will do…



All of them play their role, but SKOS might be the most understandable and the most useful technology for semantic alignment and correspondences between large vocabularies in a multilingual context.

• SKOS stands for Simple Knowledge Organisation System– it provides properties for semantic mappings between concepts of different controlled vocabularies– it’s an application of RDF



A short introduction to SKOS

SKOS is a family of formal languages designed for representation of thesauri, taxonomies, subject-heading systems, or any other type of structured controlled vocabulary. It’s main objective is to enable easy publication and connecting of controlled structured vocabularies for the Semantic Web.

It’s important to know that SKOS only provides the structure and the technology to connect data coming from different sources. Defining the semantic relations between data is still a manual work that often requires a degree of expertise in the domain of the terminology.

The process of semantically connecting data coming from different authority files like thesauri is called ‘mapping’.



SKOS Core

It defines the classes and properties sufficient to represent the common features found in a standard thesaurus. It is based on a concept-centric view of the vocabulary, where primitive objects are not terms, but abstract conceptsrepresented by terms.

Components• Concepts: Concepts can be organized in hierarchies using broader-narrower relationships, or linked by non hierarchical (associative) relationships.• Uses URIs for pointing (identifying) concepts• Labelled with lexical strings in one or more natural languages (for creating multi-lingual thesauri)• Documented with various types of note• Semantically related to each other in informal hierarchies and association networks ( -> Semantic Web)• Aggregated into concept schemes = A set of concepts, optionally including statements about semantic relationships between those concepts.



Semantic relations within a monolingual thesaurus


Relationship Abbreviation English

Hierarchical

Associative

Equivalence

Definition

BT

NT

Broader Term

Narrower Term

Related TermRT

USE

UF

Use (Preferred Term)

Used For (Non-Preferred Term)

SN Scope Note


Hierarchical relations between terms

- BT: Broader Term- NT: Narrower Term- TT: Top Term

Example:- Container (TT)

> Barrel (NT of Container)> Coffin (NT of Container)> Vessel (NT of Container – BT of Bucket, Pot,…)

>> Bucket (NT of Vessel)>> Pot (NT of Vessel – BT of Chamber pot)

>>> Chamber pot (NT of Pot)

Some terms can logically belong to more than one broader category. If the thesaurus allows a term to have more than one broader term it is said to be polyhierarcical: e.g.Organ: BT keyboard instrument; wind instrument



British museum object names thesaurus:

http://www.collectionstrust.org.uk/bmobj/Obthesm3.htm

>> Barrel and Vessel are the NT of Container





>> Vessel is the NT of Container

>> Container is the BT of Vessel

>> Pot is the NT of Vessel





>> Chamber-Pot is the NT of Pot

>> Pot is the NT of Vessel



Associative relations between terms

- RT: Related term

Example:- Chamber pot (NT of Pot)

RT: • Bed pan• Latrine• Urinal…

The associate relationship provides a way of linking terms which do not have a genuine hierarchical connection and consequently fail to qualify as broader/narrower terms



>> Bed-Pan is a RT of Chamber-Pot



Equivalence terms

- USE: Use or PT (Preferred term) = used as an index heading- UF: Used For or NP (Non-Preferred Term) = a cross reference to the equivalent preferred term

Preferred term = Standard / Indexing termNon-Preferred term = synonyms, different spellings, to help find thepreffered termThere should be sufficient entry terms to ensure that the user will be quickly directed to the correct preferred term whichever word they think of initially

Example: - Food-vessel (NT) USE Vessel (PT)- Figurine USE Statuette



>> Food-Vessel USE Vessel

>> Vessel UF Food-Vessel



1. EquivalenceThe diagram implies equivalent sets. Circle A and B overlap.Example:ancient monuments (A) USE monuments (B)monuments (B) UF ancient monuments (A)

2. HierarchicalThe diagram implies class inclusionExample:mammals (B) NT dogs (A)

3. AssociativeThe diagram implies semantic overlap, ie. there is and element of meaning common to both termsExample:gold RT money

A = B

AB

A B



Scope notes: SN

Sometimes the meaning of a term is not obvious. That’s where the importance of a Scope Note comes in:A scope note:- gives a definition or explanation about the meaning of a term - gives an indication of what the term covers - refers to related terms, synonyms,…- must be relevant as an indexing/search term

Example:Shoe:SN: Outer foot covering not reaching above the ankle. Includes additional footwear worn over normal outer foot covering such as overshoes. For devices to raise the foot clear of the mud, etc. see 'patten'.



TGN:http://www.getty.edu/research/conducting_research/vocabularies/tgn/



Why Scope Notes are so important

- Homographs: are words that are spelled the same yet have different meaning.> Example: The French term ‘Bois’ has two meanings, both ‘Wood’ and ‘Antlers’

- Appearance of the same term more than ones in the thesaurus.�Example:

Animal > Antler > AntelopeAnimal > Skin > Antelope

(French) Animal > BoisFossile > Bois (could both be Fossil wood or Fossil antlers)Végetal > Bois

Although the place of the term in the thesaurus indicates its meanig most of the time, it is best to provide a Scope Note. Especially when the thesaurus is being used in a multilingual environment. There is for example also a Brussels in Wisconsin (USA).



A good thesaurus should use Scope Notes to define its tems to prevent wrong interpretation and use of a term.

These principles can be useful in any thesaurus and, whether it is a SKOSified thesaurus or not. It just makes it possible to structure your data and by doing so getting better result when searching your database for relevant information.



Conceptually inter-connecting multiple authority files and the creation multilinguistic thesauri

SKOS provides the possibility to connect different thesauri in an online environment. It is the perfect tool for the creation of multilingual thesauri.

When semantically connecting different multilingual terminologies to each other, it is of even greater importance to know the exact meaning and covering of the term. So when creating a multilingual thesaurus, the Scope Notes should be translated as well as the terms!

Another necessity is to define the degree of the match of the term to its equivalent in another language. Two concepts are equivalent if we can fit them in the same place of a semantic network, but an exact match isn’t always possible.



Multilingual equivalencies

1 - Exact Equivalence (=)Where the target language contains a term which is:a) identical in meaning and scope to the term in the source languageb) capable of functioning as a preferred termExample: adminstration = administración

2- Inexact Equivalence ( ≅ )A term in the target language expresses the same general concept as the source languageterm, although the meaning of these terms are not precisely identicalExample: crown property ≅ patrimonio nacional

3 - Single to Multiple (A=B+C)The term in the source language cannot be matched by an exactly equivalent term in thetarget language, but the concept to which the source language term refers can be expressedby a combination of two or more existing preferred terms in the target language.Example: listed building (source) = édifice inscrit + édifice classé (target)

4 - Non-equivalenceThe target language does not contain a term which corresponds in meaning, either partiallyor inexactly, to the source language term. In this case the term from the source languagecan be:a) taken as a loan term: Example: affectataires_FR (source) affectataires_EN (target) ORb) translated from the original language: Example: patrimoine pariétal (source) parietalheritage (target)

Source language Target language



Mapping to SKOS

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

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

• skos:closeMatch and skos:exactMatch are used to assert that two concepts have a similar meaning

• skos:closeMatch is used to link two concepts that are sufficiently similar



<rdf:Description rdf:about="http://iaaa.cps.unizar.es/thesaurus/HYDROBIOLOGY"><rdf:type rdf:resource="http://www.w3.org/2004/02/skos/core#Concept"/><skos:related rdf:resource="http://iaaa.cps.unizar.es/thesaurus/AQUACULTURE"/><skos:prefLabel xml:lang="fr">HYDROBIOLOGIE</skos:prefLabel><skos:related rdf:resource="http://iaaa.cps.unizar.es/thesaurus/ENVIRONMENTAL SCIENCES"/><skos:related rdf:resource="http://iaaa.cps.unizar.es/thesaurus/AQUATIC PLANTS"/><skos:related rdf:resource="http://iaaa.cps.unizar.es/thesaurus/VIRUSES"/><skos:broader rdf:resource="http://iaaa.cps.unizar.es/thesaurus/BIOLOGY"/><skos:related rdf:resource="http://iaaa.cps.unizar.es/thesaurus/MARINE BIOLOGY"/><skos:inScheme rdf:resource="http://iaaa.unizar.es/thesaurus/UNESCO"/><skos:prefLabel xml:lang="en">HYDROBIOLOGY</skos:prefLabel><skos:related rdf:resource="http://iaaa.cps.unizar.es/thesaurus/AQUATIC ENVIRONMENT"/><skos:narrower rdf:resource="http://iaaa.cps.unizar.es/thesaurus/LIMNOLOGY"/><skos:prefLabel xml:lang="es">HIDROBIOLOGÍA</skos:prefLabel><skos:related rdf:resource="http://iaaa.cps.unizar.es/thesaurus/AQUATIC ANIMALS"/><skos:related rdf:resource="http://iaaa.cps.unizar.es/thesaurus/AQUATIC ECOSYSTEMS"/></rdf:Description>



Some well known SKOSified thesauri

• ICONCLASS (iconographic description)http://www.iconclass.org/

• Getty Arts and Architecture Thesaurus (AAT)http://www.getty.edu/research/conducting_research/vocabularies/aat/

• Getty Union List of Artist (ULAN)http://www.getty.edu/research/conducting_research/vocabularies/ulan/

• Getty Thesaurus of Geographical Names (TGN) http://www.getty.edu/research/conducting_research/vocabularies/tgn/

• The UNESCO thesaurushttp://www2.ulcc.ac.uk/unesco/

• Library of Congress Subject Headings (LCSH)



Conclusion

However powerful the software, it can only be as good as the underlying metadata and thesaurus structure. Computers can take a lot of effort out of compiling, maintaining and using the database, but they cannot make the intellectual decisions which are needed to function effectively. Without standardisation in your own collection management database, this next step of making digital cultural heritage information on the web more accessible will never be reached.

And remember that by improving your collection management database, you also improve your own search results;-)

Thank you for your attention

For more information: [email protected]



Documentation

W3C Semantic Web activity on SKOS: http://www.w3.org/2004/02/skos/

Athena website – WP4 SKOS workshop Rome 16-07-2009: http://www.athenaeurope.org/

Collections Trust: Guidelines for Constructing a Museum Object Name Thesaurushttp://www.collectionstrust.org.uk/spectrum-terminology/holm#What

Introductory Tutorial on Thesaurus Construction: Univ. of Western Ontariohttp://publish.uwo.ca/~craven/677/thesaur/main00.htm

Standard guide to establisment and development of monoloigical theasuri (BS 5723) (British Standards Institution, 1987) and the virtually identical ISO 2788.

Wikipedia:http://en.wikipedia.org/wiki/Simple_Knowledge_Organization_Systemhttp://en.wikipedia.org/wiki/Ontology_(information_science)


Thesauri and the Semantic Web

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