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Siks December 2008 History Of Knowledge Representation

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    • 1. Rinke Hoekstra
      History of Knowledge Representation
      10-12-2008
      SIKS Course - Knowledge Modelling
      1
    • 2. Caveat Emptor
      About me…
      Knowledge Engineering
      Ontologies
      Web Ontology Language (OWL 2)
      Dissertation
      Ontology Representation: Design Patterns and Ontologies that Make Sense (to be published spring 2009, I hope)
      10-12-2008
      SIKS Course - Knowledge Modelling
      2
    • 3. Overview
      In the beginning… (400 BC – 1900s)
      Scruffies vs. Neats (1970-ies)
      The Dark Ages (1980-ies)
      Engineering Revival (1990-ies)
      The ‘O’ Word (1995 onwards)
      10-12-2008
      SIKS Course - Knowledge Modelling
      3
    • 4. In the beginning…
      400BC – 1900s
      10-12-2008
      SIKS Course - Knowledge Modelling
      4
    • 5. Aristotle (384 BC – 322 BC)
      Dialectics
      reductio ad absurdum
      Deduction
      premises  conclusion (Plato)
      Syllogisms
      Standard logic until the 19th century
      Categories
      10-12-2008
      SIKS Course - Knowledge Modelling
      5
    • 6. Syllogisms
      Example
      Major premise All mortal things die
      Minor premise All men are mortal things
      Conclusion All men die
      Forms
      Names
      Barbara (AAA), Celarent (EAE), …
      10-12-2008
      SIKS Course - Knowledge Modelling
      6
    • 7. Aristotle’s Categories
      Substance
      primary vs. secondary
      Quantity
      extension
      Quality
      nature
      Relation
      Place
      position relative to environment
      Time
      pos. relative to events
      Position
      condition of rest (action)
      State
      condition of rest (affection)
      Action
      production of change
      Affection
      reception of change
      10-12-2008
      SIKS Course - Knowledge Modelling
      7
    • 8. Porphyry of Tyre (233–c. 309)
      10-12-2008
      SIKS Course - Knowledge Modelling
      8
    • 9. Brentano (1838-1917)
      10-12-2008
      SIKS Course - Knowledge Modelling
      9
    • 10. Ramon Llull (1232 – 1315)
      Mechanical aids to reasoning
      10-12-2008
      SIKS Course - Knowledge Modelling
      10
    • 11. Scientific Revolution (17th and 18th century)
      Dualism
      René Descartes (1596 – 1650)
      Body as machine <-> Mind
      Empiricism
      John Locke (1632 – 1704)
      Royal Society
      Mercantilism
      Engineering
      Christiaan Huygens (1629 – 1695)
      Blaise Pascal (1623 – 1662)
      10-12-2008
      11
    • 12. John Wilkins (1614 – 1672)
      Universal Character
      Replace latin
      (Metric system)
      Tree with 3 layers
      10-12-2008
      SIKS Course - Knowledge Modelling
      12
    • 13. Gottfried Wilhelm Leibniz (1646 – 1716)
      CharacteristicaUniversalis
      “Once the characteristic numbers of most notions are determined, the human race will have a new kind of tool, a tool that will increase the power of the mind much more than optical lenses helped our eyes, a tool that will be as far superior to microscopes or telescopes as reason is to vision.”
      (Leibniz, Philosophical Essays)
      10-12-2008
      SIKS Course - Knowledge Modelling
      13
    • 14. Calculators
      Pascaline
      Addition
      Substraction
      Stepped Reckoner
      Multiplication
      Division
      Binary System
      … but Leibniz wanted more
      Calculus Ratiocinator
      10-12-2008
      SIKS Course - Knowledge Modelling
      14
    • 15. Another Leibniz Quote
      "If controversies were to arise, there would be no more need of disputation between two philosophers than between two accountants. For it would suffice to take their pencils in their hands, and say to each other: Let us calculate.”
      Leibniz, Dissertio de Arte Combinatoria, 1666
      10-12-2008
      SIKS Course - Knowledge Modelling
      15
    • 16. Linnaeus (1707-1778) –SystemaNaturae
      10-12-2008
      SIKS Course - Knowledge Modelling
      16
    • 17. … so, what’s new?
      Syllogisms
      Rules of valid reasoning
      Reasoning as Calculation
      Symbol Manipulation
      Categories
      Top-down categories of thought
      Universal Character/SystemaNaturae
      Bottom-up inventory of phenomena in reality
      10-12-2008
      SIKS Course - Knowledge Modelling
      17
    • 18. GottlobFrege (1884 – 1924)
      Logic
      Study of correct reasoning
      Arithmetics and Mathematics
      Begriffschrift
      Formal Language (of Meaning)
      Axiomatic Predicate Logic
      Variables, Functions, Quantifiers
      10-12-2008
      SIKS Course - Knowledge Modelling
      18
    • 19. Computers
      Algorithms
      Alan Turing (1912 – 1954)
      Processor/Memory Architecture
      Neumann JánosLajos(1903 – 1957)
      Automatic Theorem Proving
      Resolution
      Artificial Intelligence!
      But…
      10-12-2008
      SIKS Course - Knowledge Modelling
      19
    • 20. Theorem Proving
      ``… great theorem proving controversy of the late sixties …’’ (Newell, 1982)
      Problematic
      No human scale
      No organisation
      No procedures
      Small, theoretically hard problems
      10-12-2008
      SIKS Course - Knowledge Modelling
      20
    • 21. Scruffies vs. Neats
      1970ies
      10-12-2008
      SIKS Course - Knowledge Modelling
      21
    • 22. Two Schools (1970ies and onwards)
      Philosophy (Neats)
      Clean, uniform language
      Knowledge derives from small set of ‘elegant’ first principles
      Theoretical understanding of reality
      Cognitive Psychology (Scruffies)
      Cognitively plausible language
      Knowledge is what’s in our heads
      Human intelligence and behaviour
      10-12-2008
      SIKS Course - Knowledge Modelling
      22
    • 23. Artificial Intelligence
      “. . . an entity is intelligent if it has an adequate modelof the world […], if it is clever enough to answer a wide variety of questions on the basis of this model, if it can get additional informationfrom the external world when required, and can perform such tasks in the external world as its goals demand and its physical abilities permit.”
      (McCarthy and Hayes, 1969, p.4)
      Frame Problem!
      10-12-2008
      SIKS Course - Knowledge Modelling
      23
    • 24. Epistemic and Heuristic adequacy
      McCarthy & Hayes:
      Representation vs. Mechanism
      Epistemic Adequacy
      Correct representation
      Heuristic Adequacy
      Correct reasoning
      10-12-2008
      SIKS Course - Knowledge Modelling
      24
    • 25. Heuristic vs. Epistemic views in Psychology
      Knowledge is about the how
      Problem Solving
       Production Systems
      Knowledge is about the what
      Natural Language
      Memory
       Semantic Networks
      10-12-2008
      SIKS Course - Knowledge Modelling
      25
    • 26. Information Processing System (IPS)
      Computer as metaphor of the mind
      “the human operates as an
      information processing machine’’
      Newell & Simon, 1972
      10-12-2008
      SIKS Course - Knowledge Modelling
      26
    • 27. Production Systems (1)
      Processor
      Interpreter
      Elementary Information Processes (EIP)
      Sequence of EIPs a function of symbols in memory
      Production Rules (Emil Post, 1943)
      if … then …
      Rule ‘fires’ if interpreter finds a match between condition and symbols in memory
      Sequential ≠ material implication
      10-12-2008
      SIKS Course - Knowledge Modelling
      27
    • 28. Production Systems (2)
      Adequacy?
      Correspondence to human reasoning
      Not ‘clean’ or ‘logical’
      Escape limitations of theorem provers
      Local, rational control of problem solving
      Easily modifiable
      Drawback: Natural language?
      10-12-2008
      SIKS Course - Knowledge Modelling
      28
    • 29. Semantic Networks (1)
      Natural Language
      Ground lexical terms in a model of reality
      Semantic Memory
      M. Ross Quillian (1966)
      Associative Memory
      Semantic Networks
      Graph Based
      Nodes, planes and pointers
      subclass, modification, disjunction, conjunction, subject/object
      10-12-2008
      SIKS Course - Knowledge Modelling
      29
    • 30. Semantic Networks (2)
      10-12-2008
      SIKS Course - Knowledge Modelling
      30
    • 31. Semantic Networks (3)
      Adequacy?
      Correspondence to human memory
      Response time
      Property inheritance
      Extensions
      Named Attributes (type/token)
      Concepts vs. Examples (instances)
      Jaime Carbonell, 1970
      Sprawl of variants
      10-12-2008
      SIKS Course - Knowledge Modelling
      31
    • 32. Frames (1)
      Criticism from Cognitive Science
      Frames, Marvin Minsky (1975)
      Scripts, Roger Schank (1975)
      Frames
      Larger `chunks’ of thought
      Situations (akin to planes)
      Default values
      10-12-2008
      SIKS Course - Knowledge Modelling
      32
    • 33. Frames (2)
      Frame system
      Related frames that share the same terminals
      … different viewpoints on the same situation
      Knowledge Reuse
      Information Retrieval Network
      Standard matching procedure
      Fixed perspective:
      situations, objects, processes
      (object-oriented design)
      10-12-2008
      SIKS Course - Knowledge Modelling
      33
    • 34. Semantic Networks (3)
      Technical problems
      Weak inference (inheritance)
      Unclear semantics
      “What’s in a link?”, Bill Woods (1975)
      “What IS-A is and isn’t”, Ron Brachman (1983)
      Consider the semantics of the representation itself
      10-12-2008
      SIKS Course - Knowledge Modelling
      34
    • 35. Frame (like) Languages
      Emphasis
      Interrelated, internallystructuredconcepts
      Knowledge Representation Language (KRL)
      Bobrow and Winograd (1976)
      Structured InheritanceNetworks
      Ron Brachman (1979)
      10-12-2008
      SIKS Course - Knowledge Modelling
      35
    • 36. Knowledge Representation Language (KRL)
      Known entity: prototype
      Description by reusable descriptors
      Descriptions by comparison to prototype + extension
      Modes of description:
      membership, relationship, role (object/event)
      Reasoning:
      Process of recognition, procedural attachments
      Inference mechanism determines meaning
      10-12-2008
      SIKS Course - Knowledge Modelling
      36
    • 37. SI Networks
      KL-ONE (Brachman, 1979; Brachman & Schmolze, 1985)
      Descriptions
      Role/Filler Descriptions
      Structural Descriptions
      Interpretive Attachments
      Role modality types:
      inherent, derivable, obligatory
      10-12-2008
      SIKS Course - Knowledge Modelling
      37
    • 38. SI-Network of an Arch
      10-12-2008
      SIKS Course - Knowledge Modelling
      38
    • 39. Epistemological Status
      Cognitive plausibility Epist. Status
      Relation to reality?
      Relation to representation language?
      10-12-2008
      SIKS Course - Knowledge Modelling
      39
    • 40. The Knowledge Level (Allen Newell, 1982)
      “… the crux for AI is that no one has been able to formulate in a reasonable way the problem of finding the good representation, so that it can be tackled by an AI system”
      (Newell, 1982, p.3)
      Computer System Level
      Medium
      System
      Processing Components
      Composition Guidelines
      Behavior
      Independent, but reducible to lower level
      10-12-2008
      SIKS Course - Knowledge Modelling
      40
    • 41. The Knowledge Level (2)
      10-12-2008
      SIKS Course - Knowledge Modelling
      41
      “There exists a distinct computer systems level, lying immediately above the symbol level, which is characterised by knowledge as the medium and the principle of rationality as the law of behaviour”
      (Newell, 1982, p. 99)
    • 42. The Knowledge Level (3)
      Not a stance
      viz. the intentional stance(Dennett, 1987)
      No representation at knowledge level
      (concepts, tasks, goals)
      Knowledge level = knowledge itself!
      Representation always at the symbol level
      Knowledge representation
      Representation of knowledge, not reality
      10-12-2008
      SIKS Course - Knowledge Modelling
      42
    • 43. Brachman’s Triangle Extended (Hoekstra, 2009)
      10-12-2008
      SIKS Course - Knowledge Modelling
      43
    • 44. Representation and Language
      Brachman’s levels in Semantic Nets
      Primitives of KR languages
      Requirements
      neutrality, adequacy, well-defined semantics
      10-12-2008
      SIKS Course - Knowledge Modelling
      44
    • 45. Epistemological Level
      Missing level
      Knowledge-structuring primitives
      “The formal structure of conceptual units and their interrelationships as conceptual units (independent of any knowledge expressed therein) forms what could be called an epistemology.”
      (Brachman, 1979, p.30)
      Two interpretations
      Adequacy of Language for some level
      Representation at a level
      e.g. Logical primitives as concepts
      10-12-2008
      SIKS Course - Knowledge Modelling
      45
    • 46. Optimism
      Modern Knowledge Representation
      Representation of expert knowledge
      Performance over Plausibility
      Modern Languages
      Defined semantics
      Clear epistemological status
      10-12-2008
      SIKS Course - Knowledge Modelling
      46
    • 47. The Dark Ages
      1980ies
      10-12-2008
      SIKS Course - Knowledge Modelling
      47
    • 48. Practical Applications (1980s)
      Expert Systems
      Production Rules
      Rules of thumb
      Relatively clear status
      Memory in PSI of secondary importance
      Severe problems
      Scalability
      Reusability
      10-12-2008
      SIKS Course - Knowledge Modelling
      48
    • 49. MYCIN and GUIDON (William Clancey, 1983)
      MYCIN: medical diagnosis
      GUIDON: medical tutoring
      “transfer back” expert knowledge
      Problematic
      No information about how the rule-base was structured: design knowledge
      “Compiled Knowledge”
      10-12-2008
      SIKS Course - Knowledge Modelling
      49
    • 50. Role of Knowledge in Problem Solving
      10-12-2008
      SIKS Course - Knowledge Modelling
      50
    • 51. Knowledge Types
      Order of rules: problem solving strategy
      Structure in knowledge
      Common causes before unusual ones
      Justification: domain theory
      Identification rules
      Causal rules
      World fact rules
      Domain fact rules
      10-12-2008
      SIKS Course - Knowledge Modelling
      51
    • 52. Convergence?
      Heuristic vs. Epistemological Adequacy
      Two approaches
      Different formalisms
      Same types of knowledge
      Two solutions
      Components (Clancey)
      Knowledge Structuring (Brachman)
      10-12-2008
      SIKS Course - Knowledge Modelling
      52
    • 53. Problems
      Knowledge Acquisition Bottleneck (Feigenbaum, 1980)
      The difficulty to correctly extract relevant knowledge from an expert into a knowledge base
      Interaction Problem (Bylander and Chandrasekaran, 1987)
      Different types of knowledge cannot be cleanly separated
      Problem for reuse
      10-12-2008
      SIKS Course - Knowledge Modelling
      53
    • 54. ENGINEERING REVIVAL
      1990s
      10-12-2008
      SIKS Course - Knowledge Modelling
      54
    • 55. Knowledge Acquisition
      Ensure
      Quality
      Reuse
      Ad hoc Methodologies
      Engineering
      Knowledge modelling vs. extraction
      Implementation guided by Specification
      10-12-2008
      SIKS Course - Knowledge Modelling
      55
    • 56. CommonKADS(Wielinga et al., 1992, van Heijst et al., 1997)
      Knowledge Level Model
      Independent of KR language
      Solution to the KA Bottleneck?
      Limited Interaction Hypothesis
      Solution to the Interaction Problem?
      10-12-2008
      SIKS Course - Knowledge Modelling
      56
    • 57. Reuse
      Role limiting
      Direct reuse
      Index symbol level representations
      Detailed blueprints
      Skeletal Models
      Reuse of `understanding’
      Knowledge-level ‘sketches’
      Library of reusable knowledge components
      10-12-2008
      SIKS Course - Knowledge Modelling
      57
    • 58. Knowledge Types (1)
      Control Knowledge
      Task Knowledge
      Inference Knowledge
      Problem Solving Methods (Breuker & van de Velde, 1994)
      10-12-2008
      SIKS Course - Knowledge Modelling
      58
    • 59. Knowledge Types (2)
      Domain Knowledge
      Index PSM’s for reuse  Epistemology
      Generic domain theory
      What an expert system ‘knows’ about:
      ONTOLOGY
      10-12-2008
      SIKS Course - Knowledge Modelling
      59
    • 60. Functional Perspective (Hector Levesque, 1984)
      Descend to the Symbol Level?
      Knowledge Base
      Abstract datatype
      Competencies
      Set of TELL/ASK queries
      Capabilities of KB
      Function of queries/answers, assertions
      10-12-2008
      SIKS Course - Knowledge Modelling
      60
    • 61. Knowledge Based Systems
      Architecture
      Specialised KR languages
      Specialised Services
      Performance guarantees
      Domain Theory
      Identification, Classification
      KL-ONE like languages…
      Control Knowledge
      Rules…
      10-12-2008
      SIKS Course - Knowledge Modelling
      61
    • 62. The return of logic (Levesque & Brachman, 1987)
      Classification as logical inference
      Exact semantics
      Trade-off
      Expressive power
      Computational efficiency
      Restricted Language Thesis
      “… general purpose knowledge representation systems should restrict their languages by omitting constructs which require non-polynomial (or otherwise unacceptably long) worst-case response times for correct classification of concepts.” (Doyle & Patil, 1991)
      10-12-2008
      SIKS Course - Knowledge Modelling
      62
    • 63. Description Logics (Baader & Hollunder, 1991)
      KL-One, NIKL, KL-Two, LOOM, FL, KANDOR, KRYPTON, CLASSIC …
      Quest
      Expressive
      Sound & Complete
      Decidable
      KRIS, SHIQ, SHOIN, SROIQ, …
      10-12-2008
      SIKS Course - Knowledge Modelling
      63
    • 64. … and the rest?
      Domain Theory
      Causal Knowledge
      Naïve Physics
      Qualitative Reasoning (J. de Kleer, K.D. Forbus, B. Bredeweg, …)
      Strategic Knowledge
      Logic-based approaches
      Prolog, Datalog, etc..
      … no principled effort.
      10-12-2008
      SIKS Course - Knowledge Modelling
      64
    • 65. The ‘O’ Word
      1995 and onwards
      10-12-2008
      SIKS Course - Knowledge Modelling
      65
      Oh no!
      Not that again!
    • 66. Pop Quiz
      What is an ontology?
      10-12-2008
      SIKS Course - Knowledge Modelling
      66
    • 67. Ontology
      “Ontology or the science of something and of nothing, of being and not-being of the thing and the mode of the thing, of substance and accident”
      G.W. Leibniz
      “… ontology, the science, namely, which is concerned with the more general properties of all things.”
      Immanuel Kant
      The nature of being
      Aristotle’s categories
      10-12-2008
      SIKS Course - Knowledge Modelling
      67
    • 68. Knowledge Representation (Davis, Shrobe, Szolovits, 1993)
      Surrogate
      Set of ontological commitments
      through language and domain theory
      Fragmentary theory of intelligent reasoning
      sanctions heuristic adequacy
      Medium for pragm. efficient computation
      way of formulating problems (Newell)
      Medium of human expression
      ``Universal Character’’(Wilkins, Leibniz, … and Stefik, 1986)
      10-12-2008
      SIKS Course - Knowledge Modelling
      68
    • 69. Ontology Definitions
      Knowledge Management
      An explicit (knowledge level) specification of a conceptualization (a.o. Gruber, 1994)
      Knowledge Representation
      An explicit (symbol level) specification of a conceptualisation
      Philosophy
      A formal specification of an ontological theory
      10-12-2008
      SIKS Course - Knowledge Modelling
      69
    • 70. The END
      10-12-2008
      SIKS Course - Knowledge Modelling
      70