march 15, 20061 dr. douglas b. lenat, 3721 executive center drive, suite 100, austin, tx 78731...
TRANSCRIPT
March 15, 2006
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Dr. Douglas B. Lenat , 3721 Executive Center Drive, Suite 100, Austin, TX 78731
Email: [email protected]
Phone: (512) 342-4001
2 July 2005
Formal Ontologies:
Upper Ontology Symposium
March 15, 2006
2
Dr. Douglas B. Lenat , 3721 Executive Center Drive, Suite 100, Austin, TX 78731
Email: [email protected]
Phone: (512) 342-4001
2 July 2005
Formal Ontologies:
Upper Ontology Symposium
Can Anything With That Title be Understandable or Interesting?
March 15, 2006
32 July 2005
Formal Ontologies:
Upper Ontology Symposium
Can Anything With That Title be Understandable or Interesting?
The basic idea:
Get the computer to understand, not just store, information. Then it can reason to answer queries, search, translate, transcribe speech, etc.
March 15, 2006
42 July 2005
The basic idea:
Get the computer to understand, not just store, information. Then it can reason to answer queries, search, translate, transcribe speech, etc.
Contrast: With NO Formal Ontology…
… programs are lucky to even have the appearance of understanding, a thin veneer of intelligence. E.g., ELIZA.
ELIZA (DOCTOR) 1965 Joe Weizenbaum, MIT
Simulation/Parody of reflection psychotherapy
Patient: “I swear a lot.”
“How do you feel about the fact that you swear a lot?”
Patient: “My dog’s mother died recently.”
“Tell me more about your mother.”
Carl Rogers
“blah blah blah mother blah blah blah”
ELIZA (DOCTOR)
Patient: “My dog’s mother died recently.”
“Tell me more about your mother.”
“blah blah blah mother blah blah blah”
ELIZA (DOCTOR)
Patient: “My dog’s mother died recently.”
“Tell me more about your mother.”
Patient: “My dog’s mother died recently.”
“Tell me more about your mother.”
“blah blah blah mother blah blah blah”
ELIZA (DOCTOR)
“blah blah blah mother blah blah blah”
ELIZA (DOCTOR)
Patient: “My dog’s mother died recently.”
“Tell me more about your mother.”
“blah blah blah mother blah blah blah”
ELIZA (DOCTOR)
Patient: “My dog’s mother died recently.”
“Tell me more about your mother.”
Patient: “My dog’s mother died recently.”
“Tell me more about your mother.”
“blah blah blah mother blah blah blah”
ELIZA (DOCTOR)
“blah blah blah mother blah blah blah”
ELIZA (DOCTOR)
Patient: “My dog’s mother died recently.”
“Tell me more about your mother.”
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ALICE (today!!)
• JUDGE11[9:26:0]I WILL ASK YOU SOME SIMPLE QUESTIONS AND GIVE SIMPLE COMMANDS
• PROGRAM[9:26:0]Do you think your plan will succeed?
• JUDGE11[9:26:16]WHAT IS THE COLOR OF A BLUE CAR• PROGRAM[9:26:16]WHAT COLOR IS a blue car
• JUDGE11[9:26:35]WHERE IS SUE’S NOSE WHEN SUE IS IN HER HOUSE
• PROGRAM[9:26:35]Where it belongs. Try searching the Web.
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From the User’s POV
• The user has a question they want answered• The data needed to answer it is available to them,
but not in one single, obvious, reliable place• The answers follow logically (and/or
arithmetically) from m elements in n sources• Don’t want to have to know, ahead of time, what
sources to go to, how to access them, how to combine the intermediate results.
• Do want to be able to limit, ahead of time, the uncertainty, recency, granularity, ideology… (and/or see such meta-level info for each answer)
“Which first-run movies star a teenager born in Texas
and are showing today at a theater < 10 minutes’ drive from this building?”
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172 July 2005
The basic idea:
Get the computer to understand, not just store, information. Then it can
reason to answer your queries.
MicrowaveOven is a type of Kitchen-Appliance
Dishwasher is a type of Kitchen-Appliance
March 15, 2006
182 July 2005
The basic idea:
Get the computer to understand, not just store, information. Then it can
reason to answer your queries.
Rthagide-disjaks is a type of Kitchen-Appliance
Gracinimumples is a type of Kitchen-Appliance
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192 July 2005
The basic idea:
Get the computer to understand, not just store, information. Then it can
reason to answer your queries.
Rthagide-disjaks is a type of Kitchen-Appliance
Gracinimumples is a type of Kitchen-Appliance
Rthagide-disjaks requires Electricity.
Gracinimumples requires Electricity and Water.
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202 July 2005
The basic idea:
Get the computer to understand, not just store, information. Then it can
reason to answer your queries.
Rthagide-disjaks is a type of Kitchen-Appliance
Gracinimumples is a type of Kitchen-Appliance
Rthagide-disjaks requires Vorawnistz.
Gracinimumples requires Vorawnistz and Buzqa.
March 15, 2006
212 July 2005
The basic idea:
Get the computer to understand, not just store, information. Then it can
reason to answer your queries.
Rthagide-disjaks is a type of Kitchen-Appliance
Gracinimumples is a type of Kitchen-Appliance
Rthagide-disjaks requires Vorawnistz.
Gracinimumples requires Vorawnistz and Buzqa.
Buzqa is a Liquid and supplied through Pipes.
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222 July 2005
The basic idea:
Get the computer to understand, not just store, information. Then it can
reason to answer your queries.
Rthagide-disjaks is a type of Kitchen-Appliance
Gracinimumples is a type of Kitchen-Appliance
Rthagide-disjaks requires Vorawnistz.
Gracinimumples requires Vorawnistz and Buzqa.
Buzqa is a Thwarn and supplied through Epluns.
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232 July 2005
The basic idea:
Get the computer to understand, not just store, information. Then it can
reason to answer your queries.
Eventually, after writing millions of these rules, the system knows as much about pipes, liquids, water, electricity, microwave ovens, dishwashers, etc. as you and I do. (In Technospeak: eventually there is just one interpretation of that model.)
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242 July 2005
The basic idea:
Get the computer to understand, not just store, information. Then it can
reason to answer your queries.
Example: Google
How could it be more powerful if it were formal (had some understanding)?
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Query: “Show me pictures of strong and adventurous people”
Caption: “A man climbing a rock face”
find information
find information
by inference (+KB)
by inference (+KB)
How formalized knowledge helps search
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Text Document
Query: “Outdoor explosions in terrorist events Lebanon between 1990 and 2001”
Document: “1993 pipe bombing on the patio of the Beirut Hilton coffee shop.”
find information
find information
by inference (+KB)
by inference (+KB)
How formalized knowledge helps search
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Text Document
Query: “Threats to low-flying US airliners in Lebanon”
Document: “Hizballah buys ten SA-7’s.”
find information
find information
by inference (+KB)
by inference (+KB)
both general and domain knowledge^ How formalized knowledge helps search
vets
Do you mean:
• vets (military veteran) • vets (veterinary surgeon)
Web Results 1 -25
New Search Revise
vets: 25,947 matches
1. Photographs of Cyclo-Vets @ work
2. Veterans National Archives
3. Recommended Vets for Hamster Owners
4. Sponsors on Vets On Line
5. Pops Place BBS Index Page
Do you mean:
• vets (military veteran) • vets (veterinary surgeon)
Web Results 1 -25
New Search Revise
vets: 25,947 matches
1. Photographs of Cyclo-Vets @ work
2. Veterans National Archives
3. Recommended Vets for Hamster Owners
4. Sponsors on Vets On Line
5. Pops Place BBS Index Page
(ex-serviceman OR "military veteran") OR vet OR veteran
AND NOT (veterinarian OR "veterinary surgeon" OR “animal doctor”
(ex-serviceman OR ”mili
Do you mean:
• vets (military veteran) • vets (veterinary surgeon)
(ex-serviceman OR ”mili
Web Results 1 -25
New Search Revise
2. Surf Point - Society & Issues: Military/Armed Forces: War Veterans
3. A Vet Remembers
4. Retail and Wholesale Merchants of Military/ Veteran Goods and Services
1. Veterans News and Information Service - Military, Army, Navy, Marine Corps, Air Force, Coast Guard
(ex-serviceman OR "military veteran") OR vet OR veteran
AND NOT (veterinarian OR "veterinary surgeon" OR “animal doctor”
vets: 25,947 matchesvets: 388,109 matches
vets
Do you mean:
• vets (military veteran) • vets (veterinary surgeon)
Web Results 1 -25
New Search Revise
vets: 25,947 matches
1. Photographs of Cyclo-Vets @ work
2. Veterans National Archives
3. Recommended Vets for Hamster Owners
4. Sponsors on Vets On Line
5. Pops Place BBS Index Page
Do you mean:
• vets (military veteran) • vets (veterinary surgeon)
Web Results 1 -25
New Search Revise
vets: 25,947 matches
1. Photographs of Cyclo-Vets @ work
2. Veterans National Archives
3. Recommended Vets for Hamster Owners
4. Sponsors on Vets On Line
5. Pops Place BBS Index Page
veterinarian OR "veterinary surgeon" OR “animal doctor”
AND NOT (ex-serviceman OR "military veteran" OR veteran)
veterinarian OR “veteri
Do you mean:
• vets (military veteran) • vets (veterinary surgeon)
Web Results 1 -25
New Search Revise
vets: 25,947 matches
1. Veterinary Book List
2. Advice from The White Cross Veterinary Group
3. Welcome to the World of Eco-Vet
4. Animal Wellness International
5. The economy or management of animals
veterinarian OR "veterinary surgeon" OR “animal doctor”
AND NOT (ex-serviceman OR "military veteran" OR veteran)
veterinarian OR “veteri
vets: 153,060 matches
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XYZCoID #
birthdate
hiredate
salu-tation
firstname
lastname
emergcontact
signifother
8041 9/1/57 8/5/91 Mr Pat Jones 8053 8053
Find and clean (consistency-check) Find and clean (consistency-check) information by inference (+KB)information by inference (+KB)
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XYZCoID #
birthdate
hiredate
salu-tation
firstname
lastname
emergcontact
signifother
8041 9/1/57 8/5/91 Mr Pat Jones 8053 8053
8053 3/3/49 2/9/48 Ms Jan Smith 8053 8199
Find and clean (consistency-check) Find and clean (consistency-check) information by inference (+KB)information by inference (+KB)
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How can our programs be intelligent, not merely have the veneer of it?
• ANSWER: By having – and being able to apply, not just store – a large corpus of knowledge, spanning the gamut from specific domain-dependent all the way up to general common sense.
• E.g., consider the task of getting a program to understand natural language. How would having lots of machine-usable knowledge help?
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Natural Language Understanding requires having lots of knowledge
1.The pen is in the box. The box is in the pen.
2. The police watched the demonstrators…
…because they feared violence.
…because they advocated violence.
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Natural Language Understanding requires having lots of knowledge
3.Mary and Sue are sisters.
Mary and Sue are mothers.
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Natural Language Understanding requires having lots of knowledge
4.Every American has a mother.
Every American has a president.
5. John saw his brother skiing on TV. The fool…
...didn’t have a coat on!
…didn’t recognize him!
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Natural Language Understanding requires having lots of knowledge
6. George Burns: “My aunt is in the hospital.
I went to see her today, and took her flowers.”
Gracie Allen: “George, that’s terrible!
You should have brought her flowers!” TookTableSanction . . .
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What is this “knowledge”?Millions of facts, rules of thumb, etc.
Represented as sentences in some language
If the language is Logic, computers can do the deductive reasoning automatically, themselves
The sentences are all composed of words; the full list of words is what we call the ontology
The sentences, expressed in logic, are formal
That’s why we call the words (terms) and logic sentences (axioms) about them a formal ontology
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Organize Terms into an Ontology
Surface Vehicle
Overland Vehicle
Water Vehicle
Surface Water VehicleSubmarine Vehicle
Vehicle
Truck#809726543
INSTANCE
Wheeled Vehicle
Truck
Railed VehicleTracked Vehicle
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Attach Facts/Rules/... to the Nodes(inherit knowledge through class hierarchy)
Surface Vehicle
Overland Vehicle
Water Vehicle
Surface Water VehicleSubmarine Vehicle
Vehicle
Truck#809726543
INSTANCE
Wheeled Vehicle
Truck
Railed VehicleTracked Vehicle
Driven by a trained adult human
Can’t control its altitude
Leaves tracks
Surface Vehicle
Overland Vehicle
Water Vehicle
Surface Water VehicleSubmarine Vehicle
Vehicle
Truck#809726543
INSTANCE
Wheeled Vehicle
Truck
Railed VehicleTracked Vehicle
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March 15, 2006
Move each rule to the best place
Surface Vehicle
Overland Vehicle
Water Vehicle
Surface Water VehicleSubmarine Vehicle
Vehicle Driven by a trained adult human
Can’t control its altitude
Truck#809726543
INSTANCE
Wheeled Vehicle
Truck Leaves tracks
Railed VehicleTracked Vehicle
Slow down in bad weather
Surface Vehicle
Overland Vehicle
Water Vehicle
Surface Water VehicleSubmarine Vehicle
Vehicle
Truck#809726543
INSTANCE
Wheeled Vehicle
Truck
Railed VehicleTracked Vehicle
45
March 15, 2006
Move each rule to the best place
Surface Vehicle
Overland Vehicle
Water Vehicle
Surface Water VehicleSubmarine Vehicle
Vehicle Driven by a trained adult human
Can’t control its altitude
Truck#809726543
INSTANCE
Wheeled Vehicle
Truck Leaves tracks
Railed VehicleTracked Vehicle
Slow down in bad weather
Surface Vehicle
Overland Vehicle
Water Vehicle
Surface Water VehicleSubmarine Vehicle
Vehicle
Truck#809726543
INSTANCE
Wheeled Vehicle
Truck
Railed VehicleTracked Vehicle
46
March 15, 2006
Surface Vehicle
Move each rule to the best place
Surface Vehicle
Overland Vehicle
Water Vehicle
Surface Water VehicleSubmarine Vehicle
Vehicle Driven by a trained adult human
Can’t control its altitude
Truck#809726543
INSTANCE
Wheeled Vehicle
Truck Leaves tracks
Railed VehicleTracked Vehicle
Slow down in bad weather
Overland Vehicle
Water Vehicle
Surface Water VehicleSubmarine Vehicle
Vehicle
Truck#809726543
INSTANCE
Wheeled Vehicle
Truck
Railed VehicleTracked Vehicle
March 15, 2006
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What do we mean “represent it in logic”?
(isa Socrates Man) “Socrates is a man”
(genls Man Mortal) “all men are mortal”
(ForAll ?x “all men are mortal” (implies (isa ?x Man) (isa ?x Mortal)))
(ForAll ?x “each person has a mother who’s a female person” (implies (isa ?x Person) (ThereExists ?y (and (isa ?y FemalePerson) (mother ?x ?y)))))
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What do we mean “it can reason”?
Simple:(isa Socrates Man)(ForAll ?x (implies (isa ?x Man) (isa ?x Mortal)))
Harder: Using general and specific knowledge: Can a can can-can?
Very complex: An example from our AKB (Analyst’s Knowledge Base)
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What do we mean “it can reason”?
Simple:(isa Socrates Man)(ForAll ?x (implies (isa ?x Man) (isa ?x Mortal)))(isa Socrates Mortal)
Harder: Using general and specific knowledge: Can a can can-can?
Very complex: An example from our AKB (Analyst’s Knowledge Base)
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Can a can can-can?
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Can a can can-can?
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What do we mean “it can reason”?
Simple:(isa Socrates Man)(ForAll ?x (implies (isa ?x Man) (isa ?x Mortal)))(isa Socrates Mortal)
Harder: Using general and specific knowledge: Can a can can-can?
Very complex: An example from our AKB (Analyst’s Knowledge Base)
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"What sequences of events could lead to
the destruction of Hoover Dam?"
“Were there any attacks on targets of symbolic value to
Muslims since 1987 on a Christian holy day?"
CycCyc
Terrorism KnowledgeTerrorism Knowledge
ReasoningModules
ReasoningModulesCycCyc ReasoningModules
ReasoningModules
Cycorp Tools For:Ontology-Building,
-Browsing, -Editing, & Fact/Rule Entry
Domain Experts Scenario
GenerationExplanation Generation
Query Formulation
Scenario Generator
Explanation Generator
Query Formulator
Others’/GOTSAnalysis and Collaboration Components
Interface to Data Repositories
Border Crossings
HIDObserva-
tions
Travel Records
Credit Card
Records
GeopoliticalData
GlobalTerrain
Data
Weather Data
Satellite Intel
HUMINTMessages
INSData
MilitaryIntel
output ofCOTS Text ExtractionSystems
SIGINTMessageContent
AKB
The Analyst’s Knowledge Base
Relational DB “projection” of the AKB
CT Analyst
Terrorism Knowledge
GeneralKnowledgeTerrorism Knowledge
Base
Terrorism Knowledge
Base)Terrorism Knowledge
GeneralKnowledge
OWL &
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An example: an analyst’s query posed as
part of HPKB (1996) that Cyc answered.
Logically and Arithmetically Combining n Pieces of Info.( )
Information from multiple sources
Knowledge about the domain in general
Commonsense knowledge about the real world
E.g., the Cyc inference engine is a community of 720 “agents” that attack every problem and, recursively, every subproblem (subgoal). One of these 720 is a general theorem prover; the others have special-purpose data structures/algorithms to handle the most important, most common cases, very fast.
E.g., Cyc’s 3M axioms are divided into thousands of contexts by:
granularity, topic, culture, geospatial place, time,...
There is no one correct monolithic ontology.
There is a correct monolithic reasoning mechanism, but it is so deadly slow that we never call on it unless we have to
Nonmonotonic (later information can show that something you earlier
believed is false after all). So the reasoning is default
(argumentation: gather up all the pro/con arguments, and
compare them).
Even though they are expressed in formal logic, most axioms state usuals, not absolute truths.
Each person had a mother who was also a person.
Syria was behind the assassination of Rafik Hariri.
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Cyc: A Large Formal Ontology
ThingThing
IntangibleThing
IntangibleThing IndividualIndividual
TemporalThing
TemporalThing
SpatialThing
SpatialThing
PartiallyTangible
Thing
PartiallyTangible
ThingPathsPaths
SetsRelations
SetsRelations
LogicMathLogicMath
HumanArtifactsHumanArtifacts
SocialRelations,
Culture
SocialRelations,
Culture
HumanAnatomy &Physiology
HumanAnatomy &Physiology
EmotionPerception
Belief
EmotionPerception
Belief
HumanBehavior &
Actions
HumanBehavior &
ActionsProductsDevices
ProductsDevices
ConceptualWorks
ConceptualWorks
VehiclesBuildingsWeapons
VehiclesBuildingsWeapons
Mechanical& Electrical
Devices
Mechanical& Electrical
Devices
SoftwareLiterature
Works of Art
SoftwareLiterature
Works of ArtLanguageLanguage
AgentOrganizations
AgentOrganizations
OrganizationalActions
OrganizationalActions
OrganizationalPlans
OrganizationalPlans
Types ofOrganizations
Types ofOrganizations
HumanOrganizations
HumanOrganizations
NationsGovernmentsGeo-Politics
NationsGovernmentsGeo-Politics
Business, Military
Organizations
Business, Military
Organizations
LawLaw
Business &CommerceBusiness &Commerce
PoliticsWarfarePoliticsWarfare
ProfessionsOccupationsProfessionsOccupations
PurchasingShopping
PurchasingShopping
TravelCommunication
TravelCommunication
Transportation& Logistics
Transportation& Logistics
SocialActivities
SocialActivities
EverydayLiving
EverydayLiving
SportsRecreation
Entertainment
SportsRecreation
Entertainment
ArtifactsArtifacts
MovementMovement
State ChangeDynamics
State ChangeDynamics
MaterialsParts
Statics
MaterialsParts
Statics
PhysicalAgents
PhysicalAgents
BordersGeometryBorders
Geometry
EventsScriptsEventsScripts
SpatialPaths
SpatialPaths
ActorsActionsActorsActions
PlansGoalsPlansGoals
TimeTime
AgentsAgents
SpaceSpace
PhysicalObjectsPhysicalObjects
HumanBeingsHumanBeings
Organ-izationOrgan-ization
HumanActivitiesHuman
Activities
LivingThingsLivingThings
SocialBehaviorSocial
Behavior
LifeFormsLife
Forms
AnimalsAnimals
PlantsPlants
EcologyEcology
NaturalGeography
NaturalGeography
Earth &Solar System
Earth &Solar System
PoliticalGeography
PoliticalGeography
WeatherWeather
General Knowledge about Various DomainsGeneral Knowledge about Various Domains
Cyc contains:15,000 Predicates
300,000 Concepts3,200,000 Assertions
Represented in:• First Order Logic• Higher Order
Logic• Context Logic• Micro-theories
Specific data, facts, and observationsSpecific data, facts, and observations
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Temporal Relations
37 Relations Between Temporal Things
temporalBoundsIntersect
temporallyIntersects
startsAfterStartingOf
endsAfterEndingOf
startingDate
temporallyContains
temporallyCooriginating
temporalBoundsContain
temporalBoundsIdentical
startsDuring
overlapsStart
startingPoint
simultaneousWith
after
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Temporal Relations
“Ariel Sharon was in Jerusalem during 2005 with granularity calendar-week”
“Condoleezza Rice made a ten-day trip to Jerusalem in February of 2005”
Both of them were in Jerusalem during February 2005
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parts
intangibleParts
subInformation
subEvents
physicalDecompositions
physicalPortions
physicalParts
externalParts
internalParts
anatomicalParts
constituents
functionalPart
Senses of ‘Part’
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Concepts in mereotopology:Concepts in mereotopology: X X is part of Y Y
X X overlaps Y Y
X isX is connected to YY
X isX is is the sum the objects YY11…Y…Ynn
These can be used to describe real world situations, e.g.These can be used to describe real world situations, e.g.
The relationship of the Sonora desert to California, Arizona and MexicoThe relationship of the Sonora desert to California, Arizona and Mexico
The Sonora desert is The Sonora desert is part of the the
sum of CA, AZ and Mexico. CA, AZ and Mexico.
Senses of ‘Part’
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23 Senses of ‘In’• Can the inner object leave by passing between
members of the outer group?– Yes -- Try in-Among
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23 Senses of ‘In’• Does part of the inner
object stick out of the container?
– None of it. -- Try in-ContCompletely
– Yes -- Try in-ContPartially
– If the container were turned around could the contained object fall out?
No -- Try
in-ContClosed
Yes -- Try in-ContOpen
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23 Senses of ‘In’ Is it attached to the inside
of the outer object?
– Yes -- Try connectedToInside
Can it be removed, if enough force is used,
without damaging either object?
– Yes -- Try in-Snugly or screwedIn
Does the inner object stick into the outer
object? Yes -- Try sticksInto
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Event Types
PhysicalStateChangeEvent TemperatureChangingProcess BiologicalDevelopmentEvent ShapeChangeEvent MovementEvent ChangingDeviceState GivingSomething DiscoveryEvent
Cracking Carving Buying Thinking Mixing Singing CuttingNails PumpingFluid
11,000 more
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performedBy causes-EventEvent objectPlaced objectOfStateChange outputsCreated inputsDestroyed assistingAgent beneficiary
fromLocation toLocation deviceUsed driverActor damages vehicle providerOfMotiveForce
transportees
Relations Between Relations Between an Event and its Participantsan Event and its Participants
Over 400 more.
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Emotion
• Types of Emotions:
– Adulation– Abhorrence– Relaxed-Feeling– Gratitude– Anticipation-Feeling
– Over 120 of these
• Predicates For Defining and Attributing Emotions:
– contraryFeelings– appropriateEmotion– actionExpressesFeeling– feelsTowardsObject– feelsTowardsPersonType
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Propositional Attitudes Relations Between Agents and Propositions
• goals• intends• desires• hopes• expects• beliefs
• opinions • knows• rememberedProp• perceivesThat• seesThat• tastesThat
Most of these are modal and assertions using them go beyond 1st-order logic
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Devices• Over 4000 Specializations
of PhysicalDevice– ClothesWasher– NuclearAircraftCarrier
• Vocabulary for Describing Device Functions– primaryFunction-DeviceType
Device Specific Predicates
• gunCaliber• speedOf
Device States (40+) DeviceOn
CockedState
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Building Cyc qua Engineering Task
amount known
rate
of
lear
ning
learning by discovery
learning via
natural language
CYC
750 person-years
21 realtime years
$75 million
Frontier of human knowledge
198
4
200
420
06
codify & enter each piece of knowledge, by hand
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(foundingDate AbuSayyaf ?X)
AKA by Shallow Fishing
Automated Knowledge Acquisition
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• Abu Sayyaf was founded in ___
• Al Harakat Islamiya, established in ___
• ASG was established in ___
Search Strings
(foundingDate AbuSayyaf ?X)
AKA by Shallow Fishing
Automated Knowledge Acquisition
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• Abu Sayyaf was founded in ___
• Al Harakat Islamiya, established in ___
• ASG was established in ___
Search Strings
(foundingDate AbuSayyaf ?X)
AKA by Shallow Fishing
Automated Knowledge Acquisition
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• Abu Sayyaf was founded in ___
• Al Harakat Islamiya, established in ___
• ASG was established in ___
Search Strings
Abu Sayyaf was founded in the early 1990s
Parse
(foundingDate AbuSayyaf (EarlyPartFn (DecadeFn 199)))
(foundingDate AbuSayyaf ?X)
AKA by Shallow Fishing
Automated Knowledge Acquisition
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(maritalStatus MohamedAtta?X)
PersonTypeByMaritalStatus
AKA by Shallow Fishing
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• (maritalStatus MohamedAtta Single)
• (maritalStatus MohamedAtta Married)
• (maritalStatus MohamedAtta Divorced) …
•(maritalStatus MohamedAtta Cohabitating-Unmarried)
(maritalStatus MohamedAtta?X)
Generate alternative assertions
PersonTypeByMaritalStatus
AKA by Shallow Fishing
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• (maritalStatus MohamedAtta Single)
• (maritalStatus MohamedAtta Married)
• (maritalStatus MohamedAtta Divorced) …
•(maritalStatus MohamedAtta Cohabitating-Unmarried)
For each one, generate a set of search strings
(maritalStatus MohamedAtta?X)
• Mohamed Atta’s fiancee
• Mohamed Atta’s wife
• Mohammed Atta’s ex-wife
• …husband, Mohamed Atta,…
Generate alternative assertions
PersonTypeByMaritalStatus
AKA by Shallow Fishing
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• (maritalStatus MohamedAtta Single)
• (maritalStatus MohamedAtta Married)
• (maritalStatus MohamedAtta Divorced) …
•(maritalStatus MohamedAtta Cohabitating-Unmarried)
For each one, generate a set of search strings
(maritalStatus MohamedAtta Married)
(maritalStatus MohamedAtta?X)
• Mohamed Atta’s fiancee
• Mohamed Atta’s wife
• Mohammed Atta’s ex-wife
• …husband, Mohamed Atta,…
Generate alternative assertions
PersonTypeByMaritalStatus
AKA by Shallow Fishing
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Harnessing Lots of Users
useful distinguishing facts
• Identify underpopulated common sense predicates• Use semantic constraints + shallow parsing to identify possible fact completions• Present multiple choice questions to novices to complete facts
150-400 commonsense GAFs/hour
Hat worn on: Head Neck Foot Leg
WWW.CYC.COM
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• bits/bytes/streams/network…• alphabet, special characters,…• words, morphological variants,…• syntactic meta-level markups (HTML)• semantic meta-level markups (SGML, XML)• content (logical representation of doc/page/...)• context (common sense, recent utterances, and n
dimensions of formal ontological knowledge: time, space, level of granularity, the source’s purpose, etc.)
What Needs to be Shared?
I.e., share a formal ontology, including a full upper ontology, large portions of a middle ontology, and relevant slivers of a lower (domain-dependent) ontology.
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Dr. Douglas B. Lenat , 3721 Executive Center Drive, Suite 100, Austin, TX 78731
Email: [email protected]
Phone: (512) 342-4001
2 July 2005
Formal Ontologies:
Upper Ontology Symposium
Can Anything With That Title be Understandable or Interesting?
I.e., share a formal ontology, including a full upper ontology, large portions of a middle ontology, and relevant slivers of a lower (domain-dependent) ontology.