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Principles for knowledge
engineering on the Web
Guus Schreiber
VU University Amsterdam
Computer Science, Web & Media
Overview of this talk
• Semantic Web: the digital heritage case
• Knowledge-engineering principles
• Challenges for Web KE
My journey
knowledge engineering
• design patterns for
problem solving
• methodology for
knowledge systems
• models of dom...
My journey
access to digital heritage
My journey
Web standards
• Web metadata: RDF
• OWL Web Ontology Language
• SKOS model for publishing vocabularies
on the W...
SEMANTIC WEB: THE
DIGITAL-HERITAGE CASE
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 h...
Vocabulary interoperability: SKOS
Vocabulary representations
• SKOS has been a major success
• Easy to understand and create
• LCSH publication set importan...
The myth of a unified vocabulary
• In large virtual collections there are always multiple
vocabularies
– In multiple langu...
Example use of vocabulary
alignment
“Tokugawa”
SVCN period
Edo
SVCN is local in-house
ethnology thesaurus
AAT style/period...
Enriching metadata with
concepts
Learning vocabulary
alignments
• Example: learning relations between art
styles and artists through NLP of art
historic te...
Semantic search: result clustering
based on retrieval path
Research issues
• Information retrieval as graph search
– more semantics => more paths
– finding optimal graph patterns
• ...
Personalized Rijksmuseum
• Interactive user
modeling
•Recommendations of
artworks and art topics
Mobile museum tour
KNOWLEDGE ENGINEERING
PRINCIPLES
Lessons I learned
Principle 1: Be modest!
• Ontology engineers should refrain from
developing their own idiosyncratic
ontologies
• Instead, ...
Principle 2: Think large!
"Once you have a truly massive amount of
information integrated as knowledge, then the
human-sof...
Principle 3: Develop and use
patterns!
• Don’t try to be (too) creative
• Ontology engineering should not be an
art but a ...
Principle 4: Don’t recreate, but
enrich and align
• Techniques:
– Learning ontology relations/mappings
– Semantic analysis...
Principle 5: Beware of ontological
over-commitment!
Principle 6: writing in an ontology
language doesn’t make it an ontology!
• Ontology is vehicle for sharing
• Papers about...
Principle 7: Required level of formal
semantics depends on the domain!
• In our semantic search we use three
OWL construct...
CHALLENGES FOR WEB KE
Challenge: Linked Open Data
Availability of government data:
http://data.gov.uk
The fight for “standard” semantics
Schema.org
Challenge: vocabulary
alignment methodology
• Multitude of alignment techniques
available
– Direct syntactic match
– Lexic...
Limitations of categorical
thinking
• The set theory on which ontology languages are
built is inadequate for modelling how...
Challenge: new types of search
exploiting semantics
Relation search:
Picasso, Matisse & Braque
Challenge: combining professional
annotations with public “tags”
Challenge: data trust issues
• How can a museum trust annotations of
outsiders?
• Need to adapt techniques from closed
wor...
Challenge: event-centred approach
=> people like narratives
Extracting piracy events
from piracy reports & Web sources
Visualising piracy events
Large-scale
experimentation!
TOWARDS WEB SCIENCE
We need to study the Web as a
phenomenon
• Web dynamics
• Collective intelligence
• Privacy, trust and
security
• Linked o...
Web for
Social
Development
48
Acknowledgements
• Long list of people
• Projects: MIA, MultiemdiaN E-Culture,
CHOICE, MunCH, CHIP, Agora,
PrestoPrime, No...
Principles for knowledge engineering on the Web
Principles for knowledge engineering on the Web
Principles for knowledge engineering on the Web
Principles for knowledge engineering on the Web
Principles for knowledge engineering on the Web
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Keynote ICK3 conference, Paris, 2011

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Principles for knowledge engineering on the Web

  1. 1. Principles for knowledge engineering on the Web Guus Schreiber VU University Amsterdam Computer Science, Web & Media
  2. 2. Overview of this talk • Semantic Web: the digital heritage case • Knowledge-engineering principles • Challenges for Web KE
  3. 3. My journey knowledge engineering • design patterns for problem solving • methodology for knowledge systems • models of domain knowledge • ontology engineering
  4. 4. My journey access to digital heritage
  5. 5. My journey Web standards • Web metadata: RDF • OWL Web Ontology Language • SKOS model for publishing vocabularies on the Web
  6. 6. SEMANTIC WEB: THE DIGITAL-HERITAGE CASE
  7. 7. The Web: resources and links URL URL Web link
  8. 8. The Semantic Web: typed resources and links URL URL Web link ULAN Henri Matisse Dublin Core creator Painting “Woman with hat” SFMOMA
  9. 9. Vocabulary interoperability: SKOS
  10. 10. Vocabulary representations • SKOS has been a major success • Easy to understand and create • LCSH publication set important example
  11. 11. 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
  12. 12. Example use of 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
  13. 13. Enriching metadata with concepts
  14. 14. Learning vocabulary alignments • Example: learning relations between art styles and artists through NLP of art historic texts – “Who are Impressionist painters?”
  15. 15. Semantic search: result clustering based on retrieval path
  16. 16. Research issues • Information retrieval as graph search – more semantics => more paths – finding optimal graph patterns • Vocabulary alignment • Information extraction – recognizing people, locations, … – identity resolution • Multi-lingual resources
  17. 17. Personalized Rijksmuseum • Interactive user modeling •Recommendations of artworks and art topics
  18. 18. Mobile museum tour
  19. 19. KNOWLEDGE ENGINEERING PRINCIPLES Lessons I learned
  20. 20. 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 an interoperable (web) format • Initially, only add the originally intended semantics
  21. 21. Principle 2: Think large! "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." Doug Lenat
  22. 22. Principle 3: Develop and use patterns! • Don’t 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/
  23. 23. Principle 4: Don’t recreate, but enrich and align • Techniques: – Learning ontology relations/mappings – Semantic analysis, e.g. OntoClean – Processing of scope notes in thesauri
  24. 24. Principle 5: Beware of ontological over-commitment!
  25. 25. Principle 6: writing in an ontology language doesn’t make it an ontology! • Ontology is vehicle for sharing • Papers about your own idiosyncratic “university ontology” should be rejected at conferences • The quality of an ontology does not depend on the number of, for example, OWL constructs used
  26. 26. Principle 7: Required level of formal semantics depends on the domain! • In our semantic search we use three OWL constructs: – owl:sameAs, owl:TransitiveProperty, owl:SymmetricProperty • But cultural heritage has is very different from medicine and bioinformatics – Don’t over-generalize on requirements for e.g. OWL
  27. 27. CHALLENGES FOR WEB KE
  28. 28. Challenge: Linked Open Data
  29. 29. Availability of government data: http://data.gov.uk
  30. 30. The fight for “standard” semantics Schema.org
  31. 31. Challenge: vocabulary alignment methodology • Multitude of alignment techniques available – Direct syntactic match – Lexical manipulation – Structured, …. • Precision & recall varies • Large evaluation initiative – OAEI http://oaei.ontologymatching.org/
  32. 32. Limitations of categorical thinking • The set theory on which ontology languages are built is inadequate for modelling how people think about categories (Lakoff) – Category boundaries are not hard: cf. art styles – People think of prototypes; some examples are very prototypical, others less • We also need to make meta-distinctions explicit – organizing class: “furniture” – base-level class: “chair” – domain-specific: “Windsor chair”
  33. 33. Challenge: new types of search exploiting semantics
  34. 34. Relation search: Picasso, Matisse & Braque
  35. 35. Challenge: combining professional annotations with public “tags”
  36. 36. Challenge: data trust issues • How can a museum trust annotations of outsiders? • Need to adapt techniques from closed world to open world • Ongoing case studies study reputation assessment, use of probability theories, ….
  37. 37. Challenge: event-centred approach => people like narratives
  38. 38. Extracting piracy events from piracy reports & Web sources
  39. 39. Visualising piracy events
  40. 40. Large-scale experimentation!
  41. 41. TOWARDS WEB SCIENCE
  42. 42. We need to study the Web as a phenomenon • Web dynamics • Collective intelligence • Privacy, trust and security • Linked open data • Universal access
  43. 43. Web for Social Development 48
  44. 44. Acknowledgements • Long list of people • Projects: MIA, MultiemdiaN E-Culture, CHOICE, MunCH, CHIP, Agora, PrestoPrime, NoTube, EuropeanaConnect, Poseidon

Keynote ICK3 conference, Paris, 2011

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