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Rinke HoekstraHistory of Knowledge Representation10-12-2008SIKS Course - Knowledge Modelling1
Caveat EmptorAbout me…Knowledge EngineeringOntologiesWeb Ontology Language (OWL 2)DissertationOntology Representation: Design Patterns and Ontologies that Make Sense (to be published spring 2009, I hope)10-12-2008SIKS Course - Knowledge Modelling2
OverviewIn the beginning… (400 BC – 1900s)Scruffies vs. Neats (1970-ies) The Dark Ages (1980-ies)Engineering Revival (1990-ies)The ‘O’ Word (1995 onwards)10-12-2008SIKS Course - Knowledge Modelling3
In the beginning…400BC – 1900s10-12-2008SIKS Course - Knowledge Modelling4
Aristotle (384 BC – 322 BC)Dialectics reductio ad absurdumDeductionpremises  conclusion (Plato)SyllogismsStandard logic until the 19th centuryCategories10-12-2008SIKS Course - Knowledge Modelling5
SyllogismsExampleMajor premise	All mortal things dieMinor premise	All men are mortal thingsConclusion		All men die FormsNamesBarbara (AAA), Celarent (EAE), …10-12-2008SIKS Course - Knowledge Modelling6
Aristotle’s CategoriesSubstanceprimary vs. secondaryQuantityextensionQualitynatureRelationPlaceposition relative to environmentTimepos. relative to eventsPositioncondition of rest (action)Statecondition of rest (affection)Actionproduction of changeAffectionreception of change10-12-2008SIKS Course - Knowledge Modelling7
Porphyry of Tyre (233–c. 309)10-12-2008SIKS Course - Knowledge Modelling8
Brentano (1838-1917)10-12-2008SIKS Course - Knowledge Modelling9
Ramon Llull (1232 – 1315)Mechanical aids to reasoning10-12-2008SIKS Course - Knowledge Modelling10
Scientific Revolution (17th and 18th century)DualismRené Descartes (1596 – 1650)Body as machine <-> MindEmpiricismJohn Locke (1632 – 1704)Royal SocietyMercantilismEngineeringChristiaan Huygens (1629 – 1695)Blaise Pascal (1623 – 1662)10-12-200811
John Wilkins (1614 – 1672)Universal CharacterReplace latin(Metric system)Tree with 3 layers10-12-2008SIKS Course - Knowledge Modelling12
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)10-12-2008SIKS Course - Knowledge Modelling13
CalculatorsPascalineAdditionSubstractionStepped ReckonerMultiplicationDivisionBinary System… but Leibniz wanted moreCalculus Ratiocinator10-12-2008SIKS Course - Knowledge Modelling14
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, 166610-12-2008SIKS Course - Knowledge Modelling15
Linnaeus (1707-1778) –SystemaNaturae10-12-2008SIKS Course - Knowledge Modelling16
… so, what’s new?SyllogismsRules of valid reasoningReasoning as CalculationSymbol ManipulationCategoriesTop-down categories of thoughtUniversal Character/SystemaNaturaeBottom-up inventory of phenomena in reality10-12-2008SIKS Course - Knowledge Modelling17
GottlobFrege (1884 – 1924)LogicStudy of correct reasoningArithmetics and MathematicsBegriffschriftFormal Language (of Meaning)Axiomatic Predicate LogicVariables, Functions, Quantifiers10-12-2008SIKS Course - Knowledge Modelling18
ComputersAlgorithmsAlan Turing (1912 – 1954)Processor/Memory ArchitectureNeumann JánosLajos(1903 – 1957)Automatic Theorem ProvingResolutionArtificial Intelligence! But…10-12-2008SIKS Course - Knowledge Modelling19
Theorem Proving``… great theorem proving controversy of the late sixties …’’ (Newell, 1982)ProblematicNo human scale  No organisationNo proceduresSmall, theoretically hard problems10-12-2008SIKS Course - Knowledge Modelling20
Scruffies vs. Neats1970ies10-12-2008SIKS Course - Knowledge Modelling21
Two Schools (1970ies and onwards)Philosophy (Neats)Clean, uniform languageKnowledge derives from small set of ‘elegant’ first principlesTheoretical understanding of realityCognitive Psychology (Scruffies)Cognitively plausible languageKnowledge is what’s in our headsHuman intelligence and behaviour10-12-2008SIKS Course - Knowledge Modelling22
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! 10-12-2008SIKS Course - Knowledge Modelling23
Epistemic and Heuristic adequacyMcCarthy & Hayes:Representation vs. MechanismEpistemic AdequacyCorrect representationHeuristic AdequacyCorrect reasoning10-12-2008SIKS Course - Knowledge Modelling24
Heuristic vs. Epistemic views in Psychology Knowledge is about the howProblem Solving Production SystemsKnowledge is about the whatNatural LanguageMemory Semantic Networks10-12-2008SIKS Course - Knowledge Modelling25
Information Processing System (IPS)Computer as metaphor of the mind“the human operates as an information processing machine’’			Newell & Simon, 197210-12-2008SIKS Course - Knowledge Modelling26
Production Systems (1)ProcessorInterpreterElementary Information Processes (EIP)Sequence of EIPs a function of symbols in memoryProduction Rules (Emil Post, 1943)if … then …Rule ‘fires’ if interpreter finds a match between condition and symbols in memorySequential ≠ material implication10-12-2008SIKS Course - Knowledge Modelling27
Production Systems (2)Adequacy?Correspondence to human reasoningNot ‘clean’ or ‘logical’Escape limitations of theorem proversLocal, rational control of problem solvingEasily modifiableDrawback: Natural language?10-12-2008SIKS Course - Knowledge Modelling28
Semantic Networks (1)Natural LanguageGround lexical terms in a model of realitySemantic MemoryM. Ross Quillian (1966)Associative MemorySemantic NetworksGraph BasedNodes, planes and pointerssubclass, modification, disjunction, conjunction, subject/object10-12-2008SIKS Course - Knowledge Modelling29
Semantic Networks (2)10-12-2008SIKS Course - Knowledge Modelling30
Semantic Networks (3)Adequacy?Correspondence to human memoryResponse timeProperty inheritanceExtensionsNamed Attributes (type/token)Concepts vs. Examples (instances)Jaime Carbonell, 1970Sprawl of variants 10-12-2008SIKS Course - Knowledge Modelling31
Frames (1)Criticism from Cognitive ScienceFrames, Marvin Minsky (1975)Scripts, Roger Schank (1975)FramesLarger `chunks’ of thoughtSituations (akin to planes)Default values10-12-2008SIKS Course - Knowledge Modelling32
Frames (2)Frame systemRelated frames that share the same terminals… different viewpoints on the same situationKnowledge ReuseInformation Retrieval NetworkStandard matching procedureFixed perspective: situations, objects, processes (object-oriented design)10-12-2008SIKS Course - Knowledge Modelling33
Semantic Networks (3)Technical problemsWeak 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 itself10-12-2008SIKS Course - Knowledge Modelling34
Frame (like) LanguagesEmphasisInterrelated, internallystructuredconceptsKnowledge Representation Language (KRL)Bobrow and Winograd (1976)Structured InheritanceNetworksRon Brachman (1979)10-12-2008SIKS Course - Knowledge Modelling35
Knowledge Representation Language (KRL)Known entity: prototypeDescription by reusable descriptorsDescriptions by comparison to prototype + extensionModes of description:membership, relationship, role (object/event)Reasoning:Process of recognition, procedural attachmentsInference mechanism determines meaning10-12-2008SIKS Course - Knowledge Modelling36
SI NetworksKL-ONE (Brachman, 1979; Brachman & Schmolze, 1985)DescriptionsRole/Filler DescriptionsStructural DescriptionsInterpretive AttachmentsRole modality types:inherent, derivable, obligatory10-12-2008SIKS Course - Knowledge Modelling37
SI-Network of an Arch10-12-2008SIKS Course - Knowledge Modelling38
Epistemological StatusCognitive plausibility Epist. StatusRelation to reality?Relation to representation language?10-12-2008SIKS Course - Knowledge Modelling39
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 LevelMediumSystemProcessing ComponentsComposition GuidelinesBehavior Independent, but reducible to lower level10-12-2008SIKS Course - Knowledge Modelling40
The Knowledge Level (2)10-12-2008SIKS Course - Knowledge Modelling41“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)
The Knowledge Level (3)Not a stanceviz. the intentional stance(Dennett, 1987)No representation at knowledge level(concepts, tasks, goals)Knowledge level = knowledge itself!Representation always at the symbol levelKnowledge representationRepresentation of knowledge, not reality10-12-2008SIKS Course - Knowledge Modelling42
Brachman’s Triangle Extended (Hoekstra, 2009)10-12-2008SIKS Course - Knowledge Modelling43
Representation and LanguageBrachman’s levels in Semantic NetsPrimitives of KR languagesRequirementsneutrality, adequacy, well-defined semantics10-12-2008SIKS Course - Knowledge Modelling44
Epistemological LevelMissing levelKnowledge-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 interpretationsAdequacy of Language for some levelRepresentation at a levele.g. Logical primitives as concepts10-12-2008SIKS Course - Knowledge Modelling45
OptimismModern Knowledge RepresentationRepresentation of expert knowledgePerformance over PlausibilityModern LanguagesDefined semanticsClear epistemological status10-12-2008SIKS Course - Knowledge Modelling46
The Dark Ages1980ies10-12-2008SIKS Course - Knowledge Modelling47
Practical Applications (1980s)Expert SystemsProduction RulesRules of thumbRelatively clear statusMemory in PSI of secondary importanceSevere problemsScalabilityReusability10-12-2008SIKS Course - Knowledge Modelling48
MYCIN and GUIDON (William Clancey, 1983)MYCIN: medical diagnosisGUIDON: medical tutoring“transfer back” expert knowledgeProblematicNo information about how the rule-base was structured: design knowledge“Compiled Knowledge”10-12-2008SIKS Course - Knowledge Modelling49
Role of Knowledge in Problem Solving10-12-2008SIKS Course - Knowledge Modelling50
Knowledge TypesOrder of rules: problem solving strategyStructure in knowledgeCommon causes before unusual onesJustification: domain theoryIdentification rulesCausal rulesWorld fact rules Domain fact rules10-12-2008SIKS Course - Knowledge Modelling51
Convergence?Heuristic vs. Epistemological AdequacyTwo approachesDifferent formalismsSame types of knowledgeTwo solutionsComponents (Clancey)Knowledge Structuring (Brachman)10-12-2008SIKS Course - Knowledge Modelling52
ProblemsKnowledge Acquisition Bottleneck (Feigenbaum, 1980)The difficulty to correctly extract relevant knowledge from an expert into a knowledge baseInteraction Problem (Bylander and Chandrasekaran, 1987)Different types of knowledge cannot be cleanly separatedProblem for reuse10-12-2008SIKS Course - Knowledge Modelling53
ENGINEERING REVIVAL1990s10-12-2008SIKS Course - Knowledge Modelling54
Knowledge AcquisitionEnsureQualityReuseAd hoc MethodologiesEngineeringKnowledge modelling vs. extractionImplementation guided by Specification10-12-2008SIKS Course - Knowledge Modelling55
CommonKADS(Wielinga et al., 1992, van Heijst et al., 1997)Knowledge Level ModelIndependent of KR languageSolution to the KA Bottleneck?Limited Interaction HypothesisSolution to the Interaction Problem?10-12-2008SIKS Course - Knowledge Modelling56
ReuseRole limitingDirect reuseIndex symbol level representationsDetailed blueprintsSkeletal ModelsReuse of `understanding’Knowledge-level ‘sketches’Library of reusable knowledge components10-12-2008SIKS Course - Knowledge Modelling57
Knowledge Types (1)Control KnowledgeTask KnowledgeInference KnowledgeProblem Solving Methods (Breuker & van de Velde, 1994)10-12-2008SIKS Course - Knowledge Modelling58
Knowledge Types (2)Domain KnowledgeIndex PSM’s for reuse  EpistemologyGeneric domain theoryWhat an expert system ‘knows’ about:ONTOLOGY10-12-2008SIKS Course - Knowledge Modelling59
Functional Perspective (Hector Levesque, 1984)Descend to the Symbol Level?Knowledge BaseAbstract datatypeCompetenciesSet of TELL/ASK queriesCapabilities of KBFunction of queries/answers, assertions10-12-2008SIKS Course - Knowledge Modelling60
Knowledge Based SystemsArchitectureSpecialised KR languagesSpecialised ServicesPerformance guaranteesDomain Theory Identification, ClassificationKL-ONE like languages… Control KnowledgeRules…10-12-2008SIKS Course - Knowledge Modelling61
The return of logic (Levesque & Brachman, 1987)Classification as logical inferenceExact semanticsTrade-offExpressive powerComputational efficiencyRestricted 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)10-12-2008SIKS Course - Knowledge Modelling62
Description Logics (Baader & Hollunder, 1991)KL-One, NIKL, KL-Two, LOOM, FL, KANDOR, KRYPTON, CLASSIC …QuestExpressiveSound & CompleteDecidableKRIS, SHIQ, SHOIN, SROIQ, …10-12-2008SIKS Course - Knowledge Modelling63
… and the rest?Domain Theory Causal KnowledgeNaïve PhysicsQualitative Reasoning (J. de Kleer, K.D. Forbus, B. Bredeweg, …)Strategic KnowledgeLogic-based approachesProlog, Datalog, etc..… no principled effort.10-12-2008SIKS Course - Knowledge Modelling64
The ‘O’ Word1995 and onwards10-12-2008SIKS Course - Knowledge Modelling65Oh no! Not that again!
Pop QuizWhat is an ontology?10-12-2008SIKS Course - Knowledge Modelling66
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 KantThe nature of beingAristotle’s categories10-12-2008SIKS Course - Knowledge Modelling67
Knowledge Representation (Davis, Shrobe, Szolovits, 1993)SurrogateSet of ontological commitmentsthrough language and domain theoryFragmentary theory of intelligent reasoningsanctions heuristic adequacyMedium for pragm. efficient computationway of formulating problems (Newell)Medium of human expression``Universal Character’’(Wilkins, Leibniz, … and Stefik, 1986)10-12-2008SIKS Course - Knowledge Modelling68
Ontology DefinitionsKnowledge ManagementAn explicit (knowledge level) specification of a conceptualization (a.o. Gruber, 1994)Knowledge RepresentationAn explicit (symbol level) specification of a conceptualisationPhilosophyA formal specification of an ontological theory10-12-2008SIKS Course - Knowledge Modelling69

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Siks December 2008 History Of Knowledge Representation

  • 1. Rinke HoekstraHistory of Knowledge Representation10-12-2008SIKS Course - Knowledge Modelling1
  • 2. Caveat EmptorAbout me…Knowledge EngineeringOntologiesWeb Ontology Language (OWL 2)DissertationOntology Representation: Design Patterns and Ontologies that Make Sense (to be published spring 2009, I hope)10-12-2008SIKS Course - Knowledge Modelling2
  • 3. OverviewIn the beginning… (400 BC – 1900s)Scruffies vs. Neats (1970-ies) The Dark Ages (1980-ies)Engineering Revival (1990-ies)The ‘O’ Word (1995 onwards)10-12-2008SIKS Course - Knowledge Modelling3
  • 4. In the beginning…400BC – 1900s10-12-2008SIKS Course - Knowledge Modelling4
  • 5. Aristotle (384 BC – 322 BC)Dialectics reductio ad absurdumDeductionpremises  conclusion (Plato)SyllogismsStandard logic until the 19th centuryCategories10-12-2008SIKS Course - Knowledge Modelling5
  • 6. SyllogismsExampleMajor premise All mortal things dieMinor premise All men are mortal thingsConclusion All men die FormsNamesBarbara (AAA), Celarent (EAE), …10-12-2008SIKS Course - Knowledge Modelling6
  • 7. Aristotle’s CategoriesSubstanceprimary vs. secondaryQuantityextensionQualitynatureRelationPlaceposition relative to environmentTimepos. relative to eventsPositioncondition of rest (action)Statecondition of rest (affection)Actionproduction of changeAffectionreception of change10-12-2008SIKS Course - Knowledge Modelling7
  • 8. Porphyry of Tyre (233–c. 309)10-12-2008SIKS Course - Knowledge Modelling8
  • 10. Ramon Llull (1232 – 1315)Mechanical aids to reasoning10-12-2008SIKS Course - Knowledge Modelling10
  • 11. Scientific Revolution (17th and 18th century)DualismRené Descartes (1596 – 1650)Body as machine <-> MindEmpiricismJohn Locke (1632 – 1704)Royal SocietyMercantilismEngineeringChristiaan Huygens (1629 – 1695)Blaise Pascal (1623 – 1662)10-12-200811
  • 12. John Wilkins (1614 – 1672)Universal CharacterReplace latin(Metric system)Tree with 3 layers10-12-2008SIKS Course - Knowledge Modelling12
  • 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)10-12-2008SIKS Course - Knowledge Modelling13
  • 14. CalculatorsPascalineAdditionSubstractionStepped ReckonerMultiplicationDivisionBinary System… but Leibniz wanted moreCalculus Ratiocinator10-12-2008SIKS Course - Knowledge Modelling14
  • 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, 166610-12-2008SIKS Course - Knowledge Modelling15
  • 17. … so, what’s new?SyllogismsRules of valid reasoningReasoning as CalculationSymbol ManipulationCategoriesTop-down categories of thoughtUniversal Character/SystemaNaturaeBottom-up inventory of phenomena in reality10-12-2008SIKS Course - Knowledge Modelling17
  • 18. GottlobFrege (1884 – 1924)LogicStudy of correct reasoningArithmetics and MathematicsBegriffschriftFormal Language (of Meaning)Axiomatic Predicate LogicVariables, Functions, Quantifiers10-12-2008SIKS Course - Knowledge Modelling18
  • 19. ComputersAlgorithmsAlan Turing (1912 – 1954)Processor/Memory ArchitectureNeumann JánosLajos(1903 – 1957)Automatic Theorem ProvingResolutionArtificial Intelligence! But…10-12-2008SIKS Course - Knowledge Modelling19
  • 20. Theorem Proving``… great theorem proving controversy of the late sixties …’’ (Newell, 1982)ProblematicNo human scale No organisationNo proceduresSmall, theoretically hard problems10-12-2008SIKS Course - Knowledge Modelling20
  • 21. Scruffies vs. Neats1970ies10-12-2008SIKS Course - Knowledge Modelling21
  • 22. Two Schools (1970ies and onwards)Philosophy (Neats)Clean, uniform languageKnowledge derives from small set of ‘elegant’ first principlesTheoretical understanding of realityCognitive Psychology (Scruffies)Cognitively plausible languageKnowledge is what’s in our headsHuman intelligence and behaviour10-12-2008SIKS Course - Knowledge Modelling22
  • 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! 10-12-2008SIKS Course - Knowledge Modelling23
  • 24. Epistemic and Heuristic adequacyMcCarthy & Hayes:Representation vs. MechanismEpistemic AdequacyCorrect representationHeuristic AdequacyCorrect reasoning10-12-2008SIKS Course - Knowledge Modelling24
  • 25. Heuristic vs. Epistemic views in Psychology Knowledge is about the howProblem Solving Production SystemsKnowledge is about the whatNatural LanguageMemory Semantic Networks10-12-2008SIKS Course - Knowledge Modelling25
  • 26. Information Processing System (IPS)Computer as metaphor of the mind“the human operates as an information processing machine’’ Newell & Simon, 197210-12-2008SIKS Course - Knowledge Modelling26
  • 27. Production Systems (1)ProcessorInterpreterElementary Information Processes (EIP)Sequence of EIPs a function of symbols in memoryProduction Rules (Emil Post, 1943)if … then …Rule ‘fires’ if interpreter finds a match between condition and symbols in memorySequential ≠ material implication10-12-2008SIKS Course - Knowledge Modelling27
  • 28. Production Systems (2)Adequacy?Correspondence to human reasoningNot ‘clean’ or ‘logical’Escape limitations of theorem proversLocal, rational control of problem solvingEasily modifiableDrawback: Natural language?10-12-2008SIKS Course - Knowledge Modelling28
  • 29. Semantic Networks (1)Natural LanguageGround lexical terms in a model of realitySemantic MemoryM. Ross Quillian (1966)Associative MemorySemantic NetworksGraph BasedNodes, planes and pointerssubclass, modification, disjunction, conjunction, subject/object10-12-2008SIKS Course - Knowledge Modelling29
  • 30. Semantic Networks (2)10-12-2008SIKS Course - Knowledge Modelling30
  • 31. Semantic Networks (3)Adequacy?Correspondence to human memoryResponse timeProperty inheritanceExtensionsNamed Attributes (type/token)Concepts vs. Examples (instances)Jaime Carbonell, 1970Sprawl of variants 10-12-2008SIKS Course - Knowledge Modelling31
  • 32. Frames (1)Criticism from Cognitive ScienceFrames, Marvin Minsky (1975)Scripts, Roger Schank (1975)FramesLarger `chunks’ of thoughtSituations (akin to planes)Default values10-12-2008SIKS Course - Knowledge Modelling32
  • 33. Frames (2)Frame systemRelated frames that share the same terminals… different viewpoints on the same situationKnowledge ReuseInformation Retrieval NetworkStandard matching procedureFixed perspective: situations, objects, processes (object-oriented design)10-12-2008SIKS Course - Knowledge Modelling33
  • 34. Semantic Networks (3)Technical problemsWeak 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 itself10-12-2008SIKS Course - Knowledge Modelling34
  • 35. Frame (like) LanguagesEmphasisInterrelated, internallystructuredconceptsKnowledge Representation Language (KRL)Bobrow and Winograd (1976)Structured InheritanceNetworksRon Brachman (1979)10-12-2008SIKS Course - Knowledge Modelling35
  • 36. Knowledge Representation Language (KRL)Known entity: prototypeDescription by reusable descriptorsDescriptions by comparison to prototype + extensionModes of description:membership, relationship, role (object/event)Reasoning:Process of recognition, procedural attachmentsInference mechanism determines meaning10-12-2008SIKS Course - Knowledge Modelling36
  • 37. SI NetworksKL-ONE (Brachman, 1979; Brachman & Schmolze, 1985)DescriptionsRole/Filler DescriptionsStructural DescriptionsInterpretive AttachmentsRole modality types:inherent, derivable, obligatory10-12-2008SIKS Course - Knowledge Modelling37
  • 38. SI-Network of an Arch10-12-2008SIKS Course - Knowledge Modelling38
  • 39. Epistemological StatusCognitive plausibility Epist. StatusRelation to reality?Relation to representation language?10-12-2008SIKS Course - Knowledge Modelling39
  • 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 LevelMediumSystemProcessing ComponentsComposition GuidelinesBehavior Independent, but reducible to lower level10-12-2008SIKS Course - Knowledge Modelling40
  • 41. The Knowledge Level (2)10-12-2008SIKS Course - Knowledge Modelling41“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 stanceviz. the intentional stance(Dennett, 1987)No representation at knowledge level(concepts, tasks, goals)Knowledge level = knowledge itself!Representation always at the symbol levelKnowledge representationRepresentation of knowledge, not reality10-12-2008SIKS Course - Knowledge Modelling42
  • 43. Brachman’s Triangle Extended (Hoekstra, 2009)10-12-2008SIKS Course - Knowledge Modelling43
  • 44. Representation and LanguageBrachman’s levels in Semantic NetsPrimitives of KR languagesRequirementsneutrality, adequacy, well-defined semantics10-12-2008SIKS Course - Knowledge Modelling44
  • 45. Epistemological LevelMissing levelKnowledge-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 interpretationsAdequacy of Language for some levelRepresentation at a levele.g. Logical primitives as concepts10-12-2008SIKS Course - Knowledge Modelling45
  • 46. OptimismModern Knowledge RepresentationRepresentation of expert knowledgePerformance over PlausibilityModern LanguagesDefined semanticsClear epistemological status10-12-2008SIKS Course - Knowledge Modelling46
  • 47. The Dark Ages1980ies10-12-2008SIKS Course - Knowledge Modelling47
  • 48. Practical Applications (1980s)Expert SystemsProduction RulesRules of thumbRelatively clear statusMemory in PSI of secondary importanceSevere problemsScalabilityReusability10-12-2008SIKS Course - Knowledge Modelling48
  • 49. MYCIN and GUIDON (William Clancey, 1983)MYCIN: medical diagnosisGUIDON: medical tutoring“transfer back” expert knowledgeProblematicNo information about how the rule-base was structured: design knowledge“Compiled Knowledge”10-12-2008SIKS Course - Knowledge Modelling49
  • 50. Role of Knowledge in Problem Solving10-12-2008SIKS Course - Knowledge Modelling50
  • 51. Knowledge TypesOrder of rules: problem solving strategyStructure in knowledgeCommon causes before unusual onesJustification: domain theoryIdentification rulesCausal rulesWorld fact rules Domain fact rules10-12-2008SIKS Course - Knowledge Modelling51
  • 52. Convergence?Heuristic vs. Epistemological AdequacyTwo approachesDifferent formalismsSame types of knowledgeTwo solutionsComponents (Clancey)Knowledge Structuring (Brachman)10-12-2008SIKS Course - Knowledge Modelling52
  • 53. ProblemsKnowledge Acquisition Bottleneck (Feigenbaum, 1980)The difficulty to correctly extract relevant knowledge from an expert into a knowledge baseInteraction Problem (Bylander and Chandrasekaran, 1987)Different types of knowledge cannot be cleanly separatedProblem for reuse10-12-2008SIKS Course - Knowledge Modelling53
  • 55. Knowledge AcquisitionEnsureQualityReuseAd hoc MethodologiesEngineeringKnowledge modelling vs. extractionImplementation guided by Specification10-12-2008SIKS Course - Knowledge Modelling55
  • 56. CommonKADS(Wielinga et al., 1992, van Heijst et al., 1997)Knowledge Level ModelIndependent of KR languageSolution to the KA Bottleneck?Limited Interaction HypothesisSolution to the Interaction Problem?10-12-2008SIKS Course - Knowledge Modelling56
  • 57. ReuseRole limitingDirect reuseIndex symbol level representationsDetailed blueprintsSkeletal ModelsReuse of `understanding’Knowledge-level ‘sketches’Library of reusable knowledge components10-12-2008SIKS Course - Knowledge Modelling57
  • 58. Knowledge Types (1)Control KnowledgeTask KnowledgeInference KnowledgeProblem Solving Methods (Breuker & van de Velde, 1994)10-12-2008SIKS Course - Knowledge Modelling58
  • 59. Knowledge Types (2)Domain KnowledgeIndex PSM’s for reuse  EpistemologyGeneric domain theoryWhat an expert system ‘knows’ about:ONTOLOGY10-12-2008SIKS Course - Knowledge Modelling59
  • 60. Functional Perspective (Hector Levesque, 1984)Descend to the Symbol Level?Knowledge BaseAbstract datatypeCompetenciesSet of TELL/ASK queriesCapabilities of KBFunction of queries/answers, assertions10-12-2008SIKS Course - Knowledge Modelling60
  • 61. Knowledge Based SystemsArchitectureSpecialised KR languagesSpecialised ServicesPerformance guaranteesDomain Theory Identification, ClassificationKL-ONE like languages… Control KnowledgeRules…10-12-2008SIKS Course - Knowledge Modelling61
  • 62. The return of logic (Levesque & Brachman, 1987)Classification as logical inferenceExact semanticsTrade-offExpressive powerComputational efficiencyRestricted 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)10-12-2008SIKS Course - Knowledge Modelling62
  • 63. Description Logics (Baader & Hollunder, 1991)KL-One, NIKL, KL-Two, LOOM, FL, KANDOR, KRYPTON, CLASSIC …QuestExpressiveSound & CompleteDecidableKRIS, SHIQ, SHOIN, SROIQ, …10-12-2008SIKS Course - Knowledge Modelling63
  • 64. … and the rest?Domain Theory Causal KnowledgeNaïve PhysicsQualitative Reasoning (J. de Kleer, K.D. Forbus, B. Bredeweg, …)Strategic KnowledgeLogic-based approachesProlog, Datalog, etc..… no principled effort.10-12-2008SIKS Course - Knowledge Modelling64
  • 65. The ‘O’ Word1995 and onwards10-12-2008SIKS Course - Knowledge Modelling65Oh no! Not that again!
  • 66. Pop QuizWhat is an ontology?10-12-2008SIKS Course - Knowledge Modelling66
  • 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 KantThe nature of beingAristotle’s categories10-12-2008SIKS Course - Knowledge Modelling67
  • 68. Knowledge Representation (Davis, Shrobe, Szolovits, 1993)SurrogateSet of ontological commitmentsthrough language and domain theoryFragmentary theory of intelligent reasoningsanctions heuristic adequacyMedium for pragm. efficient computationway of formulating problems (Newell)Medium of human expression``Universal Character’’(Wilkins, Leibniz, … and Stefik, 1986)10-12-2008SIKS Course - Knowledge Modelling68
  • 69. Ontology DefinitionsKnowledge ManagementAn explicit (knowledge level) specification of a conceptualization (a.o. Gruber, 1994)Knowledge RepresentationAn explicit (symbol level) specification of a conceptualisationPhilosophyA formal specification of an ontological theory10-12-2008SIKS Course - Knowledge Modelling69
  • 70. The END10-12-2008SIKS Course - Knowledge Modelling70