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The Art of Knowledge Engineering
or: knowledge engineering of art
Guus Schreiber
Knowledge engineering:
From art to discipline
■ Objective of knowledge engineering (KE) in 90’s
■ Use of knowledge patterns for “expert” tasks
The popularity of “ontology”
■ One of the first ontologies: traffic ontology
– “On representational promiscuity”
– “Vacuous paper with no content” (Brian Gaines, KAW 93)
■ Now seen as panacea for the Holy Grail of
information integration
■ There is even a Web language for it
■ But will it stand the test of time?
– are our current conceptions of formal classes and
properties sufficient to grasp the complexity of the Web?
The Web: knowledge engineering
for the masses
■ KE is outside the former small research community
■ Everyone is building hierarchies and describes classes
Intermezzo:
Knowledge democracy
■ From a human-rights point of view the Web is a leap
forward
■ Possibility for “everyone” to access knowledge
– the empowered citizen
■ But history will tell whether this remains true
Web KE builds on a long tradition of
vocabulary construction
Semantic annotation is now feasible
Associative reasoning:
following Semantic Web links
The popularity of
“ontology” alignment
■ Creating the (missing) links the the Linked Open Data
Cloud
■ Multitude of alignment techniques available
■ Large evaluation initiative
– OAEI
■ But will our alignment methods stand the test of
time? Are the results good enough?
Semantic search types
■ DISAMBIGUATE: Can you give me alternative
interpretations of term T?
■ DESCRIBE: Can you give me more information about
concept C or Individual I?
■ QUESTION ANSWERING: does property P hold for
object O?
■ ANSWER QUESTIONING: Jeopardy!
■ RELATION SEARCH: in what way(s) are object O1 and
O2 related?
Use cases for semantic search
Picasso and Moulin de la Galette
Location-based:
relatively easy
How are Picasso and Matisse
connected: Georges Braque
Style- and time-based
not trivial
How are Picasso and Matisse
connected: 1907
the changes in the art world in Paris anno 1907
difficult
Matisse and “les fauves”
Where does this term come from?
1905: the story of the critic
difficult
The importance of narratives
■ Users like to get the “stories” behind the navigation
paths in the graph
■ For this we need to have some minimal
“understanding” of the meaning of the paths
■ Well-constructed minimal ontologies can provide
such interpretations
– graph patterns
■ Example: an event ontology
Web mining of piracy events
from piracy reports & Web sources
van Hage, Malaisé & van Erp, 2011
Problems in ontology alignment
■ We have not agreed on an adequate alignment
vocabulary
– misuse of owl:sameAs
■ We have no adequate methodology for evaluating
alignments (Tordai et al., 2011)
■ In particular, people do not agree on how different
classes align
– and this is not because they don’t do it “right”
Beyond categories
■ 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”
KE for the Web: the way forward?!
■ We are only scratching the surface in semantic
search
– large-scale experimentation needed
– small minimal ontologies acting as search patterns
■ We need a revised alignment vocabulary
– taking Lakoff’s notions into account
■ Attention for semantic detail matters
– in search, in aligment, for story telling
– and lay knowledge engineers are providing it!
■ Combining this with statistical technqiues is a
powerful combination
Are Picasso and Herengracht 196
related?

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The artof of knowledge engineering, or: knowledge engineering of art

  • 1. The Art of Knowledge Engineering or: knowledge engineering of art Guus Schreiber
  • 2. Knowledge engineering: From art to discipline ■ Objective of knowledge engineering (KE) in 90’s ■ Use of knowledge patterns for “expert” tasks
  • 3. The popularity of “ontology” ■ One of the first ontologies: traffic ontology – “On representational promiscuity” – “Vacuous paper with no content” (Brian Gaines, KAW 93) ■ Now seen as panacea for the Holy Grail of information integration ■ There is even a Web language for it ■ But will it stand the test of time? – are our current conceptions of formal classes and properties sufficient to grasp the complexity of the Web?
  • 4. The Web: knowledge engineering for the masses ■ KE is outside the former small research community ■ Everyone is building hierarchies and describes classes
  • 5. Intermezzo: Knowledge democracy ■ From a human-rights point of view the Web is a leap forward ■ Possibility for “everyone” to access knowledge – the empowered citizen ■ But history will tell whether this remains true
  • 6. Web KE builds on a long tradition of vocabulary construction
  • 7. Semantic annotation is now feasible
  • 9. The popularity of “ontology” alignment ■ Creating the (missing) links the the Linked Open Data Cloud ■ Multitude of alignment techniques available ■ Large evaluation initiative – OAEI ■ But will our alignment methods stand the test of time? Are the results good enough?
  • 10. Semantic search types ■ DISAMBIGUATE: Can you give me alternative interpretations of term T? ■ DESCRIBE: Can you give me more information about concept C or Individual I? ■ QUESTION ANSWERING: does property P hold for object O? ■ ANSWER QUESTIONING: Jeopardy! ■ RELATION SEARCH: in what way(s) are object O1 and O2 related?
  • 11. Use cases for semantic search
  • 12. Picasso and Moulin de la Galette Location-based: relatively easy
  • 13. How are Picasso and Matisse connected: Georges Braque Style- and time-based not trivial
  • 14. How are Picasso and Matisse connected: 1907 the changes in the art world in Paris anno 1907 difficult
  • 15. Matisse and “les fauves” Where does this term come from? 1905: the story of the critic difficult
  • 16. The importance of narratives ■ Users like to get the “stories” behind the navigation paths in the graph ■ For this we need to have some minimal “understanding” of the meaning of the paths ■ Well-constructed minimal ontologies can provide such interpretations – graph patterns ■ Example: an event ontology
  • 17. Web mining of piracy events from piracy reports & Web sources van Hage, MalaisĂ© & van Erp, 2011
  • 18. Problems in ontology alignment ■ We have not agreed on an adequate alignment vocabulary – misuse of owl:sameAs ■ We have no adequate methodology for evaluating alignments (Tordai et al., 2011) ■ In particular, people do not agree on how different classes align – and this is not because they don’t do it “right”
  • 19. Beyond categories ■ 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”
  • 20. KE for the Web: the way forward?! ■ We are only scratching the surface in semantic search – large-scale experimentation needed – small minimal ontologies acting as search patterns ■ We need a revised alignment vocabulary – taking Lakoff’s notions into account ■ Attention for semantic detail matters – in search, in aligment, for story telling – and lay knowledge engineers are providing it! ■ Combining this with statistical technqiues is a powerful combination
  • 21. Are Picasso and Herengracht 196 related?