Thesauri and the Semantic Web

eContentplus

Roxanne Wyns
Royal Museums of Art and History

Thesauri and the Semantic Web

Brussels
16th of December 2009
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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

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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…

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

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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

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Solution

Providing semantic relations between concepts with different lexical labels

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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.

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So to support exchange of data on the web, we need a simple language for
expressing information in machine-understandable way
To 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.

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The W3C World Wide Web Consortium provides technologies to make data
integration possible
In short:
The Semantic Web “layer cake"”
Semantic Web is ...
a metadata based infrastructure for
reasoning on the Web
an extension, not a replacement of the
current web

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

Technological standards
RDF, OWL, SKOS,…
…just a technical aspect

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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.

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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 v1.0

Europeana Thought lab = Task of EuropeanaConnect Work Package 1 & 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: http://europeana.eu/portal/thought-lab.html
Contains data of: Rijksmuseum Amsterdam, Musée du Louvre, Rijksbureau voor
Kunsthistorische Documentatie
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Europeana Thought lab online:
http://europeana.eu/portal/thought-lab.html

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Semantic auto-completion

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Clustering of results

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Matching concepts’ labels

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A concept more specific than Egypte

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A concept more specific than Egypte

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Following other relations - creator

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Following other relations – creator death place

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Following other relations – creator death place

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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 Object
Leonardo authorOf Gioconda
Cimabue 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.

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• OWL (Web Ontology Language) provides a more expressive language to enhance
the exchange of information
An example in OWL:
The statement
The painting of the Sistine Chapel was carried out by Michelangelo Buonarroti
Abstracting from the statement
The painting of the Sistine Chapel (the subject) is an (instance of) activity
carried out by is a predicate
Michelangelo Buonarroti is an (instance of) Person
In OWL (conceptually)
the paintingOfSistineChapel (E7.Activity) was carried_out_by (P14F)
MichelangeloBuonarroti (E21.Person)
In OWL (graphically)
paintingOfSistineChapel
carried_out_by
MichelangeloBuonarroti

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

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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’.

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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 concepts
represented 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.

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Semantic relations within a monolingual thesaurus

Relationship Abbreviation English
BT Broader Term
Hierarchical
NT Narrower Term

Associative RT Related Term

USE Use (Preferred Term)
Equivalence
UF Used For (Non-Preferred Term)

Definition SN Scope Note

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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
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British museum object names thesaurus:
http://www.collectionstrust.org.uk/bmobj/Obthesm3.htm

>> Barrel and Vessel are the NT of Container

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>> Vessel is the NT of Container
>> Container is the BT of Vessel
>> Pot is the NT of Vessel

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>> Chamber-Pot is the NT of Pot
>> Pot is the NT of Vessel

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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

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

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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 term
Non-Preferred term = synonyms, different spellings, to help find the
preffered term
There 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

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>> Food-Vessel USE Vessel
>> Vessel UF Food-Vessel

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

2. Hierarchical
The diagram implies class inclusion
Example: B A
mammals (B) NT dogs (A)

3. Associative
The diagram implies semantic overlap,
ie. there is and element of meaning
common to both terms A B
Example:
gold RT money

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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'.

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

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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 > Antelope
Animal > Skin > Antelope

(French) Animal > Bois
Fossile > 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).
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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.

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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.

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Multilingual equivalencies Source language Target language

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

2- Inexact Equivalence ( ≅ )
A term in the target language expresses the same general concept as the source language
term, although the meaning of these terms are not precisely identical
Example: 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 the
target language, but the concept to which the source language term refers can be expressed
by a combination of two or more existing preferred terms in the target language.
Example: listed building (source) = édifice inscrit + édifice classé (target)

4 - Non-equivalence
The target language does not contain a term which corresponds in meaning, either partially
or inexactly, to the source language term. In this case the term from the source language
can be:
a) taken as a loan term: Example: affectataires_FR (source) affectataires_EN (target) OR
b) translated from the original language: Example: patrimoine pariétal (source) parietal
heritage (target)
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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

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<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>

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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 thesaurus
http://www2.ulcc.ac.uk/unesco/

• Library of Congress Subject Headings (LCSH)
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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: r.wyns@kmkg.be

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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 Thesaurus
http://www.collectionstrust.org.uk/spectrum-terminology/holm#What

Introductory Tutorial on Thesaurus Construction: Univ. of Western Ontario
http://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_System
http://en.wikipedia.org/wiki/Ontology_(information_science)
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Thesauri and the Semantic Web

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