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Principles andPrinciples and
pragmatics of apragmatics of a
Semantic Culture WebSemantic Culture Web
Tearing down walls
and
Building bridges
• Part of large Dutch
knowledge-economy
project MultimediaN
• Partners: VU, CWI, UvA,
DEN,ICN
• People:
Alia Amin, Lora Aroyo, Mark van
Assem, Victor de Boer, Lynda
Hardman, Michiel Hildebrand, Laura
Hollink, Marco de Niet, Borys
Omelayenko, Marie-France van
Orsouw, Jacco van Ossenbruggen,
Guus Schreiber Jos Taekema,
Annemiek Teesing, Anna Tordai, Jan
Wielemaker, Bob Wielinga
• Artchive.com, Rijksmuseum
Amsterdam, Dutch ethnology
musea (Amsterdam, Leiden),
National Library (Bibliopolis)
Acknowledgements
The Web:
resources and links
URL URL
Web link
The Semantic Web:
typed resources and links
URL URL
Web link
ULAN
Henri Matisse
Dublin Core
creator
Painting
“Woman with hat
SFMOMA
 Principles and pragmatics of a Semantic Culture Web
 Principles and pragmatics of a Semantic Culture Web
Principle 1: semantic annotation
• Description of
web objects with
“concepts” from
a shared
vocabulary
Principle 2: semantic search
• Search for objects
which are linked via
concepts (semantic
link)
• Use the type of
semantic link to
provide meaningful
presentation of the
search results
Paris
Montmartre
PartOf
Query
“Paris”
The myth of a unified vocabulary
• In large virtual collections there are always multiple
vocabularies
– In multiple languages
• Every vocabulary has its own perspective
– You can’t just merge them
• But you can use vocabularies jointly by defining a
limited set of links
– “Vocabulary alignment”
• It is surprising what you can do with just a few links
Principle 3: vocabulary alignment
“Tokugawa”
SVCN period
Edo
SVCN is local in-house
ethnology thesaurus
AAT style/period
Edo (Japanese period)
Tokugawa
AAT is Getty’s
Art & Architecture Thesaurus
A link between two thesauri
Levels of interoperability
• Syntactic interoperability
– using data formats that you can share
– XML family is the preferred option
• Semantic interoperability
– How to share meaning / concepts
– Technology for finding and representing semantic
links
 Principles and pragmatics of a Semantic Culture Web
 Principles and pragmatics of a Semantic Culture Web
Cross collection search
http://e-culture.multimedian.nl
Term disambiguation is key issue in
semantic search
• Post-query
– Sort search results based on different meanings
of the search term
– Mimics Google-type search
• Pre-query
– Ask user to disambiguate by displaying list of
possible meanings
– Interface is more complex, but more search
functionality can be offered
 Principles and pragmatics of a Semantic Culture Web
Keyword search with semantic
clustering
1. Btree of literals plus Porter stem and
metaphone index
2. Find resources with matching labels
• Default resources are “Work”s
3. Find related resources by one-way graph
traversal
• owl:inverseOf is used
• Threshold used for constraining search
4. Cluster results (group instances)
Faceted search Faceted
search
What do you need to do to make
your collection part of a Semantic
Culture Web?
Four activities
From metadata to
semantic metadata
1. Make vocabulary
interoperable
2. Align metadata
schema
3. Enrich
metadata
4. Align
vocabulary
Activity 1: making vocabulary
interoperabie
• Make your vocabularies available in the Web
standard RDF
• Many organizations are already do this
• W3C provides the SKOS template to make
this almost straightforward
• Effort required: at most a few days
Activity 2: aligning the metadata
schema
• Specify your collection metadata scheme as
a specialization of Dublin Core
• With RDF/OWL this is easy/trivial!
• Cf. DC Application Profiles
Aligning VRA with Dublin Core
• VRA is specialization of Dublin Core for
visual resources
• VRA properties “material.medium” and
“material.support” are specializations of
Dublin Core property “format”
vra:material.medium rdfs:subPropertyOf
dc:fotmat .
vra:material.medium rdfs:subPropertyOf
dc:format .
Activity 3: enriching the metadata
• Extracting additional concepts from an
annotation
– Matching the string “Paris” to a vocabulary term
• Information-extraction techniques exists (and
continue to be developed)
• Effort required can be up to a few weeks
– The more concepts, the better, but no need to be
perfect!
Example textual annotation
Resulting semantic annotation
(rendered as HTML with RDFa)
Activity 4: aligning the vocabulary
• Find semantic links between vocabulary links
– Derain (ULAN) related-to Fauve (AAT))
• Automatic techniques exists, but performance varies
• Often combination of automatic and manual
alignment
• Effort strongly dependent on vocabularies
– But “a little semantic goes a long way” (Hendler)
Learning alignments
• Learning relations between art styles in AAT
and artists in ULAN through NLP of art
historic texts
– “Who are Impressionist painters?”
Extracting additional knowledge
from scope notes
Perspectives
• Basic Semantic Web technology is ready for
deployment
• Web 2.0 facilities fit well:
– Involving community experts in annotation
– Personalization, myArt
• Social barriers have to be overcome!
– “open door” policy
– Involvement of general public => issues of
“quality”
Planned: annotation interfaces
• Brings in issues w.r.t. trust and access
control
– Quality issue
• Involving users in the annotation process
– Stimulating people to contribute
• User profiling
Planned: large-scale deployment
• Moving to 100-150M triples, 30+ collections
• Scalability is key
– Load balancing, 32+Gb RAM
– Search algorithms
• Methodology for including new collections
• Continuous user and usability studies

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Principles and pragmatics of a Semantic Culture Web

  • 1. Principles andPrinciples and pragmatics of apragmatics of a Semantic Culture WebSemantic Culture Web Tearing down walls and Building bridges
  • 2. • Part of large Dutch knowledge-economy project MultimediaN • Partners: VU, CWI, UvA, DEN,ICN • People: Alia Amin, Lora Aroyo, Mark van Assem, Victor de Boer, Lynda Hardman, Michiel Hildebrand, Laura Hollink, Marco de Niet, Borys Omelayenko, Marie-France van Orsouw, Jacco van Ossenbruggen, Guus Schreiber Jos Taekema, Annemiek Teesing, Anna Tordai, Jan Wielemaker, Bob Wielinga • Artchive.com, Rijksmuseum Amsterdam, Dutch ethnology musea (Amsterdam, Leiden), National Library (Bibliopolis) Acknowledgements
  • 3. The Web: resources and links URL URL Web link
  • 4. The Semantic Web: typed resources and links URL URL Web link ULAN Henri Matisse Dublin Core creator Painting “Woman with hat SFMOMA
  • 7. Principle 1: semantic annotation • Description of web objects with “concepts” from a shared vocabulary
  • 8. Principle 2: semantic search • Search for objects which are linked via concepts (semantic link) • Use the type of semantic link to provide meaningful presentation of the search results Paris Montmartre PartOf Query “Paris”
  • 9. The myth of a unified vocabulary • In large virtual collections there are always multiple vocabularies – In multiple languages • Every vocabulary has its own perspective – You can’t just merge them • But you can use vocabularies jointly by defining a limited set of links – “Vocabulary alignment” • It is surprising what you can do with just a few links
  • 10. Principle 3: vocabulary alignment “Tokugawa” SVCN period Edo SVCN is local in-house ethnology thesaurus AAT style/period Edo (Japanese period) Tokugawa AAT is Getty’s Art & Architecture Thesaurus
  • 11. A link between two thesauri
  • 12. Levels of interoperability • Syntactic interoperability – using data formats that you can share – XML family is the preferred option • Semantic interoperability – How to share meaning / concepts – Technology for finding and representing semantic links
  • 16. Term disambiguation is key issue in semantic search • Post-query – Sort search results based on different meanings of the search term – Mimics Google-type search • Pre-query – Ask user to disambiguate by displaying list of possible meanings – Interface is more complex, but more search functionality can be offered
  • 18. Keyword search with semantic clustering 1. Btree of literals plus Porter stem and metaphone index 2. Find resources with matching labels • Default resources are “Work”s 3. Find related resources by one-way graph traversal • owl:inverseOf is used • Threshold used for constraining search 4. Cluster results (group instances)
  • 20. What do you need to do to make your collection part of a Semantic Culture Web? Four activities
  • 21. From metadata to semantic metadata 1. Make vocabulary interoperable 2. Align metadata schema 3. Enrich metadata 4. Align vocabulary
  • 22. Activity 1: making vocabulary interoperabie • Make your vocabularies available in the Web standard RDF • Many organizations are already do this • W3C provides the SKOS template to make this almost straightforward • Effort required: at most a few days
  • 23. Activity 2: aligning the metadata schema • Specify your collection metadata scheme as a specialization of Dublin Core • With RDF/OWL this is easy/trivial! • Cf. DC Application Profiles
  • 24. Aligning VRA with Dublin Core • VRA is specialization of Dublin Core for visual resources • VRA properties “material.medium” and “material.support” are specializations of Dublin Core property “format” vra:material.medium rdfs:subPropertyOf dc:fotmat . vra:material.medium rdfs:subPropertyOf dc:format .
  • 25. Activity 3: enriching the metadata • Extracting additional concepts from an annotation – Matching the string “Paris” to a vocabulary term • Information-extraction techniques exists (and continue to be developed) • Effort required can be up to a few weeks – The more concepts, the better, but no need to be perfect!
  • 28. Activity 4: aligning the vocabulary • Find semantic links between vocabulary links – Derain (ULAN) related-to Fauve (AAT)) • Automatic techniques exists, but performance varies • Often combination of automatic and manual alignment • Effort strongly dependent on vocabularies – But “a little semantic goes a long way” (Hendler)
  • 29. Learning alignments • Learning relations between art styles in AAT and artists in ULAN through NLP of art historic texts – “Who are Impressionist painters?”
  • 31. Perspectives • Basic Semantic Web technology is ready for deployment • Web 2.0 facilities fit well: – Involving community experts in annotation – Personalization, myArt • Social barriers have to be overcome! – “open door” policy – Involvement of general public => issues of “quality”
  • 32. Planned: annotation interfaces • Brings in issues w.r.t. trust and access control – Quality issue • Involving users in the annotation process – Stimulating people to contribute • User profiling
  • 33. Planned: large-scale deployment • Moving to 100-150M triples, 30+ collections • Scalability is key – Load balancing, 32+Gb RAM – Search algorithms • Methodology for including new collections • Continuous user and usability studies