Principles%20and%20pragmatics%20of%20a%20Semantic%20Culture%20Web

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Principles and pragmatics of a Semantic Culture
Web
Tearing down walls and Building bridges
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Overview

• Virtual collections and Semantic Web
• Semantic collection-search demonstrator
• For cultural heritage objects
• Metadata vocabulary representation and
  enrichment
• Principles for knowledge engineering on the Web

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Acknowledgements

• 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)

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Hypothesis

• Semantic Web technology is in particular useful
  in knowledge-rich domains
• or formulated differently
• If we cannot show added value in knowledge-rich
  domains, then it may have no value at all

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The Web resources and links
Web link
URL
URL
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The Semantic Web typed resources and links
Painting Woman with hat SFMOMA
Dublin Core creator
ULAN Henri Matisse
Web link
URL
URL
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Principle 1 semantic annotation

• Description of web objects with concepts from a
  shared vocabulary

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Principle 2 semantic search
Query Paris

• 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
PartOf
Montmartre
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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 cant 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

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Principle 3 vocabulary alignment
Tokugawa
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A link between two thesauri
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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

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Distributed vs. centralized collection data

• Minimal requirement collection object has image
  URI
• Preference for external metadata, accessed
  through protocol such as OAI
• In practice, external metadata access is still
  cumbersome

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http//e-culture.multimedian.nl/demo/search
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Search strategies

• Basic search keyword-oriented
• Advanced search
• Tweaking default search parameters
• Time-related queries
• Faceted search
• Relation search
• How are two URIs related?

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Keyword search with semantic clustering

• Btree of literals plus Porter stem and metaphone
  index
• Find resources with matching labels
• Default resources are Works
• Find related resources by one-way graph traversal
• owlinverseOf is used
• Threshold used for constraining search
• Cluster results (group instances)

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Search WordNet patterns that increase recall
without sacrificing precisions
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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

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Faceted search

• Use Dublin Core scheme to formulate complex
  queries
• Navigate through relevant metadata

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Faceted search
Faceted search
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What do you need to do to make your collection
part of a Semantic Culture Web?

• Four activities

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From metadata to semantic metadata
1. Make vocabulary interoperable
2. Align metadata schema
3. Enrich metadata
4. Align vocabulary
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Activity 1 syntactic vocabulary interoperability

• Making vocabularies available in the Web standard
  RDF
• Many organizations already do this
• W3C provides the SKOS template to make this
  almost straightforward
• Effort required at most a few days

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Multi-lingual labels for concepts
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Semantic relationbroader and narrower

• No subclass semantics assumed!

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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

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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
• vramaterial.medium rdfssubPropertyOf dcfotmat
  .
• vramaterial.medium rdfssubPropertyOf dcformat .

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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!

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Example textual annotation
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Resulting semantic annotation (rendered as HTML
with RDFa)
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RDFa embedding RDF in (X)HTML
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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)

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Learning alignments

• Learning relations between art styles in AAT and
  artists in ULAN through NLP of art historic texts
• Who are Impressionist painters?

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Extracting additional knowledge from scope notes
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Principles for knowledge engineering on the Web
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Principle 1 Be modest!

• Ontology engineers should refrain from developing
  their own idiosyncratic ontologies
• Instead, they should make the available rich
  vocabularies, thesauri and databases available in
  web format
• Initially, only add the originally intended
  semantics

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Principle 2 Think large!
Doug Lenat
"Once you have a truly massive amount of
information integrated as knowledge, then the
human-software system will be superhuman, in the
same sense that mankind with writing is
superhuman compared to mankind before writing."
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Principle 3 Develop and use patterns!

• Dont try to be (too) creative
• Ontology engineering should not be an art but a
  discipline
• Patterns play a key role in methodology for
  ontology engineering
• See for example patterns developed by the W3C
  Semantic Web Best Practices group
• http//www.w3.org/2001/sw/BestPractices/
• SKOS can also be considered a pattern

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Principle 4 Dont recreate, but enrich and align

• Techniques
• Learning ontology relations/mappings
• Semantic analysis, e.g. OntoClean
• Processing of scope notes in thesauri

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Principle 5 Beware of ontologicalover-commitment
!
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Principle 6 Specifying a data model in OWL does
ot make it an ontology!

• Papers about your own idiosyncratic university
  ontology should be rejected at SW conferences
• The qality of an ontology does not depend on the
  number of OWL constrcts sed

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Principle 7 Required level of formal semantics
depends on the domain!

• In our semantic search we use three OWL
  constructs
• owlsameAs, owlTransitiveProperty,
  owlSymmetricProperty
• But cultural heritage has is very different from
  medicine and bioinformatics
• Dont over-generalize on requirements for e.g.
  OWL

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Perspectives

• Basic Semantic Web technology is ready for
  deployment
• Research themes
• Scalability, vocabulary alignment, metadata
  extraction
• Web 2.0 facilities fit well
• Involving community experts in annotation
• Personalization
• Social barriers have to be overcome!