Getting Cyc-ed about Inference

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Getting Cyc-ed about Inference

• Christopher Cox

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What is Cyc?

• The Worlds Leading Provider of Formalized
  Common Sense
• (currently 200,000 terms each with several
  assertions over 1,000,000 rules )

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What is Cyc?

• Founded in 1984 by Stanford professor Doug Lenat,
  it was a project in the MCC (Microelectronics and
  Computer Technology Corporation) until 1994, when
  Lenat left to form Cycorp
• Objective Codify the millions of pieces of
  knowledge that comprise common sense
• When people die, they stop buying things
• Kerosene flows downhill
• When a bowl is overturned, its contents fall out.

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Common Sense

• Cycs stated goal
• Break the software brittleness bottleneck
  once and for all by constructing a foundation of
  basic common-sense knowledge-a semantic
  substratum of terms, rules and relations, a deep
  layer of understanding that can be used by other
  programs to make them more flexible.
• Basic Common-Sense Knowledge
• "In modern America, this encompasses recent
  history and current affairs, everyday physics,
  household chemistry, famous books and movies and
  songs and ads, famous people, nutrition,
  addition, weather, etc

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Overview

• What is Cyc
• OpenCyc, ResearchCyc, Full Cyc
• Whats in Cyc?
• The Big Picture
• Microtheories
• Predicates and Functions
• Arguments and Types
• Lexicon
• How do I use it?
• Cyc at Stanford
• Cyc Browser
• Java and Applications

Several examples and images come from the more
extensive, online OpenCyc Tutorial www.cyc.com/doc
/tut
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Whats in Cyc?

• A Knowledge Base (KB) consisting of terms
• Dog, DogFood, Doghouse, SnoopDoggyDogg
• Assertions that relate these terms.
• Ground Assertions
• (isa MyDogSharkey BelgianSheepdog)
• (genls BelgianSheepdog Dog)
• Rules, which derive assertions from Ground
  Assertions
• (isa THING COL )
• (genls COL SUPERCOL) ---gt
• (isa THING SUPERCOL)

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The Knowledge Base
Upper Ontology Abstract Concepts
EVENT ? TEMPORAL-THING ? INDIVIDUAL ? THING
Core Theories Space, Time, Causality,
Knowledge Base Layers
For all events a and b, a causes b implies a
precedes b
Domain-Specific Theories
For any mammal m and any anthrax bacteria a, ms
being exposed to a causes m to be infected by a.

Facts Instances
John is a person infected by anthrax.
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A Dog is a ..

• Agent Agent-Generic AirBreathingVertabrate
  Animal Agent Agent-Generic AirBreathingVertabrate
  Animal AnimalBLOBilateralObject
  BiologicalLivingObect CanineAnimal
  CarnivoreCarnivoreOrder ChordataPhylum Coelmates
  Container-Underspecified Dog EukaryoticOrganism
  Eutheria FrontAndBackSidedObject Heterotroph
  HexelateralObjectHomeotherm HumanScaleObject
  Individual IndividualAgentLeftAndRightSidedObject
  Location-Underspecified MammalNaturalTangibleStuff
  NonPersonAnimal OrganicStuff Organism-Whole
  PartiallyTangible PerceptualAgent
  Region-UnderspecifiedSentientAnimal
  SolidTangibleThing SomethingExistingSpatialThing
  SpatialThing-Localized System-GenericTemporalThing
  TerrestrialOrganism ThingTopAndBottomSidedObject
  Trajector-Underspecified VertebrateAnimalBLOBila
  teralObject BiologicalLivingObect CanineAnimal
  CarnivoreCarnivoreOrder ChordataPhylum Coelmates
  Container-Underspecified Dog EukaryoticOrganism
  EutheriaFrontAndBackSidedObject Heterotroph
  HexelateralObjectHomeotherm HumanScaleObject
  Individual IndividualAgentLeftAndRightSidedObject
  Location-Underspecified MammalNaturalTangibleStuff
  NonPersonAnimal OrganicStuff Organism-Whole
  PartiallyTangible PerceptualAgent
  Region-UnderspecifiedSentientAnimal
  SolidTangibleThing SomethingExistingSpatialThing
  SpatialThing-Localized System-GenericTemporalThing
  TerrestrialOrganism ThingTopAndBottomSidedObject
  Trajector-Underspecified Vertebrate

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Microtheories

• A way of grouping assertions and rules which
  share a set of assumptions about a domain, level
  of detail, period in time, source, topic, etc.
• Each KB assertion occurs within some microtheory
• These allow for a KB that copes with global
  inconsistency and that can focus inference
  according to necessary detail

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Microtheories

• Though no monotonic contradictions are allowed
  inside a microtheory, assertions in different
  microtheries may be inconsistent
• Time
• MT1 Mandela is an elder statesman
• MT2 Mandela is the President of South Africa
• MT3 Mandela is a political prisoner
• Granularity/domain
• MT1 Tables are solid
• MT2 Tables are mostly space
• Microtheories are arranged in an inheritance
  heirarchy

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Microtheory Inheritance genlMt
BaseKB
genlMt
genlMt
NaiveSpatialMt
MovementMt
genlMt
genlMt
genlMt
NaivePhysicsMt
NaturalGeographyMt
genlMt
TransportationMt
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Predicates and Denotational Functions

• Predicates are truth-functional relations which
  can be evaluated according to facts in the KB and
  used to make sentences that are true or false
• Usually Lowercase
• (objectHasColor BrownDog Brown)
• (memberStatusOfOrganization Norway NATO
  FoundingMember)
• Functions take arguments to denote Non-Atomic
  Terms (NATs), expressions that represent things
• Usually Uppercase
• (FruitFn AppleTree) denotes an apple
• (BorderBetweenFn Sweden Norway) denotes the
  border between Sweden and Norway.

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Arity and Argument Types

• Every predicate or function is defined with
  particular arity and argument types
• Arity Number of Arguments
• (arity mother 2) (arity MotherFn 1)
• Argument Types use isa and genl relations
• (arg1Isa mother Animal)
• (arg2Isa mother FemaleAnimal)
• (arg1Isa TransportViaFn ExistingObjectType)
• (arg1Genl treatmentTypeAppliedToConditionType
• MedicalTreatmentEvent)

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Predicates and Rules

• Can be built to form meaningful, well-formed
  logical sentences
• You can add your own, using ASSERT

Mt AgentGMt Rule (implies (and
(isa ?HELP HelpingAnAgent)
(performedBy ?HELP ?HELPER)
(beneficiary ?HELP ?HELPED)
(positiveVestedInterest ?HELPER ?HELPED)
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Specialized Content

• Cyc has several specialized and useful areas of
  KB content
• Times and Dates temporallyIntersects,startsAfterSt
  artingOf,YearsDuration
• Spacial Properties and Relations
• constituent, ingredient, 60 in predicates,
• 60 Shape Attributes
• Event Types, with Roles and Actors
• MovementEvent, MedicalTreatmentEvent,
  GivingSomething

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The Cyc Lexicon

• Cyc also knows a lot about English
• There are entries for Lexical items as well
• Treat-TheWord Use-TheWord
• Several predicates express relationships which
  translate English expressions into CycL (and vice
  versa)

(verbSemTrans Use-TheWord 0 TransitiveNPFrame
(and (isa ACTION UsingAnObject)
(performedBy ACTION SUBJECT)
(instrument-Generic ACTION OBJECT)))
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Important Lexical Predicates

• denotation -- Relates a LexicalWord and
  SpeechPart to some denotedThing (e.g. some
  Individual or Collection).
• multiWordString -- Relates a list of strings
  (e.g. ("hot")), a LexicalWord (e.g. Dog-TheWord),
  and a SpeechPart to some denoted Thing (e.g.
  HotDog) c.f. MultiWord -PhrasePrediciate.
• verbSemTrans -- Relates a LexicalWord, sense
  number, and SubcategorizationFrame to a
  NLTemplateExpression c.f. SemTransPredicate.
• nameString -- Relates a Thing to a string which
  (conventionally) refers to it
• Well do some examples

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The Cyc Browser

• To run the Cyc KB Browser
• Run an image on a ja- machine.
• Move to /scr/nlp/src/cyc/cyc1.0enterprise/
• Run ./run-cyc.sh , a Cyc will start to run on
  your desktop.
• You can use the SubL interactor directly at the
  prompt
• Or you can load up a browser from the ja- machine
  (youll need to forward the desktop image to your
  machine) and set the address to
• http//localhost3602/cgi-bin/cyccgi/cg?cb-start

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Exploring Cyc

• http//researchcyc.cyc.com/
• Playing around with the Browser is only way to
  really learn whats in Cyc.
• Logging In
• The Search Box
• The Heirarchy Browser
• Documentation (usr/pass rcyc/rcyc)
• Ask
• Assert
• Query
• Toolbar
• Dont use the parser

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Example Application Cyc in RTE

• Were looking at using Cyc the context of
  Recognizing Textual Entailment
• Dependency parses are a good starting point for
  Cyc

(PID 702, Hypothesis) In the late 1980s Budapest
became the center of the reform movement.
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RTE in a Nutshell
bought
object
subj
Synonym Match Cost 0.2
Chris (person)
car
Exact Match Cost 0.0
Hypernym Match Cost 0.4
purchased
object
subj
BMW
Chris (person)
Vertex Cost (0.0 0.2 0.4)/3 0.2 Relation
Cost 0 (Graphs Isomorphic) Match Cost
0.55 (0.2) (.45) 0.0 0.11
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Cyc and Java

• We clearly need a way to interact with the Cyc KB
  programatically
• Cyc APIs exist for Java and Python
• (check out /src/nlp/src/cyc/api/java/OpenCyc.jar)
• Documentation is sparse
• Cyc could be really valuable, if we can figure
  out a way to get around whats missing
• Ive got code (soon to be in JavaNLP) for generic
  interactions with the CycKB, and for searching
  Cyc space along genls relationships as a measure
  of verb similarity
• Its a huge KB, so use your imagination

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Thank You