March 15, Dr. Douglas B. Lenat, 3721 Executive Center Drive, Suite 100, Austin, TX Phone: (512) July 2005 Formal Ontologies: Upper Ontology Symposium

March 15, Dr. Douglas B. Lenat, 3721 Executive Center Drive, Suite 100, Austin, TX Phone: (512) July 2005 Formal Ontologies: Upper Ontology Symposium Can Anything With That Title be Understandable or Interesting?

March 15, 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, 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 dogs mother died recently. Tell me more about your mother. Carl Rogers

blah blah blah mother blah blah blah ELIZA (DOCTOR) Patient: My dogs mother died recently. Tell me more about your mother.

blah blah blah mother blah blah blah ELIZA (DOCTOR) Patient: My dogs mother died recently. Tell me more about your mother.

Patient: My dogs 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 dogs mother died recently. Tell me more about your mother.

blah blah blah mother blah blah blah ELIZA (DOCTOR) Patient: My dogs mother died recently. Tell me more about your mother.

Patient: My dogs 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 dogs mother died recently. Tell me more about your mother.

March 15, 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 SUES NOSE WHEN SUE IS IN HER HOUSE PROGRAM[9:26:35]Where it belongs. Try searching the Web.

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March 15, From the Users 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 Dont 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?

March 15, 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, 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

March 15, 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.

March 15, 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, 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.

March 15, 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.

March 15, 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.)

March 15, 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)?

March 15, Query: Show me pictures of strong and adventurous people Caption: A man climbing a rock face find information by inference (+KB) How formalized knowledge helps search

March 15, 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 by inference (+KB) How formalized knowledge helps search

March 15, Text Document Query: Threats to low-flying US airliners in Lebanon Document: Hizballah buys ten SA-7s. find information 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 New Search Revise vets: 25,947 matches 1. Photographs of 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 New Search Revise vets: 25,947 matches 1. Photographs of 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 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 New Search Revise vets: 25,947 matches 1. Photographs of 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 New Search Revise vets: 25,947 matches 1. Photographs of 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 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

March 15, Find and clean (consistency-check) information by inference (+KB)

March 15, Find and clean (consistency-check) information by inference (+KB)

March 15, 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?

March 15, 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.

March 15, Natural Language Understanding requires having lots of knowledge 3.Mary and Sue are sisters. Mary and Sue are mothers.

March 15, 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…...didnt have a coat on! …didnt recognize him!

March 15, 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, thats terrible! You should have brought her flowers! Took Table Sanction.

March 15, 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 Thats why we call the words (terms) and logic sentences (axioms) about them a formal ontology

March 15, Organize Terms into an Ontology Surface Vehicle Overland Vehicle Water Vehicle Surface Water VehicleSubmarine Vehicle Vehicle Truck# INSTANCE Wheeled Vehicle Truck Railed VehicleTracked Vehicle

March 15, Attach Facts/Rules/... to the Nodes (inherit knowledge through class hierarchy) Surface Vehicle Overland Vehicle Water Vehicle Surface Water VehicleSubmarine Vehicle Vehicle Truck# INSTANCE Wheeled Vehicle Truck Railed VehicleTracked Vehicle Driven by a trained adult human Cant control its altitude Leaves tracks Surface Vehicle Overland Vehicle Water Vehicle Surface Water VehicleSubmarine Vehicle Vehicle Truck# INSTANCE Wheeled Vehicle Truck Railed VehicleTracked Vehicle

March 15, Move each rule to the best place Surface Vehicle Overland Vehicle Water Vehicle Surface Water VehicleSubmarine Vehicle Vehicle Driven by a trained adult human Cant control its altitude Truck# 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# INSTANCE Wheeled Vehicle Truck Railed VehicleTracked Vehicle

March 15, Move each rule to the best place Surface Vehicle Overland Vehicle Water Vehicle Surface Water VehicleSubmarine Vehicle Vehicle Driven by a trained adult human Cant control its altitude Truck# 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# INSTANCE Wheeled Vehicle Truck Railed VehicleTracked Vehicle

March 15, 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 Cant control its altitude Truck# INSTANCE Wheeled Vehicle Truck Leaves tracks Railed VehicleTracked Vehicle Slow down in bad weather Overland Vehicle Water Vehicle Surface Water VehicleSubmarine Vehicle Vehicle Truck# INSTANCE Wheeled Vehicle Truck Railed VehicleTracked Vehicle

March 15, 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 whos a female person (implies (isa ?x Person) (ThereExists ?y (and (isa ?y FemalePerson) (mother ?x ?y)))))

March 15, 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 (Analysts Knowledge Base)

March 15, 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 (Analysts Knowledge Base)

March 15, Can a can can-can?

March 15, Can a can can-can?

March 15, 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 (Analysts Knowledge Base)

March 15, "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?" Cycorp Tools For: Ontology-Building, -Browsing, -Editing, & Fact/Rule Entry Domain Experts Scenario Generation Explanation Generation Query Formulation Scenario Generator Explanation Generator Query Formulator Others/GOTS Analysis and Collaboration Components AKB The Analysts Knowledge Base Relational DB projection of the AKB CT Analyst Terrorism Knowledge General Knowledge OWL &

March 15, An example: an analysts 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., Cycs 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.

March 15, Cyc: A Large Formal Ontology Thing Intangible Thing Intangible Thing Individual Temporal Thing Temporal Thing Spatial Thing Spatial Thing Partially Tangible Thing Partially Tangible Thing Paths Sets Relations Sets Relations Logic Math Logic Math Human Artifacts Human Artifacts Social Relations, Culture Social Relations, Culture Human Anatomy & Physiology Human Anatomy & Physiology Emotion Perception Belief Emotion Perception Belief Human Behavior & Actions Human Behavior & Actions Products Devices Products Devices Conceptual Works Conceptual Works Vehicles Buildings Weapons Vehicles Buildings Weapons Mechanical & Electrical Devices Mechanical & Electrical Devices Software Literature Works of Art Software Literature Works of Art Language Agent Organizations Agent Organizations Organizational Actions Organizational Actions Organizational Plans Organizational Plans Types of Organizations Types of Organizations Human Organizations Human Organizations Nations Governments Geo-Politics Nations Governments Geo-Politics Business, Military Organizations Business, Military Organizations Law Business & Commerce Business & Commerce Politics Warfare Politics Warfare Professions Occupations Professions Occupations Purchasing Shopping Purchasing Shopping Travel Communication Travel Communication Transportation & Logistics Transportation & Logistics Social Activities Social Activities Everyday Living Everyday Living Sports Recreation Entertainment Sports Recreation Entertainment Artifacts Movement State Change Dynamics State Change Dynamics Materials Parts Statics Materials Parts Statics Physical Agents Physical Agents Borders Geometry Borders Geometry Events Scripts Events Scripts Spatial Paths Spatial Paths Actors Actions Actors Actions Plans Goals Plans Goals Time Agents Space Physical Objects Physical Objects Human Beings Human Beings Organ- ization Organ- ization Human Activities Human Activities Living Things Living Things Social Behavior Social Behavior Life Forms Life Forms Animals Plants Ecology Natural Geography Natural Geography Earth & Solar System Earth & Solar System Political Geography Political Geography Weather General Knowledge about Various Domains Cyc contains: 15,000Predicates 300,000Concepts 3,200,000Assertions Represented in: First Order Logic Higher Order Logic Context Logic Micro-theories Specific data, facts, and observations

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March 15, Temporal Relations 37 Relations Between Temporal Things temporalBoundsIntersect temporallyIntersects startsAfterStartingOf endsAfterEndingOf startingDate temporallyContains temporallyCooriginating temporalBoundsContain temporalBoundsIdentical startsDuring overlapsStart startingPoint simultaneousWith after

March 15, 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

March 15, parts intangibleParts subInformation subEvents physicalDecompositions physicalPortions physicalParts externalParts internalParts anatomicalParts constituents functionalPart Senses of Part

March 15, Concepts in mereotopology: X Y X is part of Y X Y X overlaps Y X is Y X is connected to Y X is Y 1 …Y n X is is the sum the objects Y 1 …Y n These can be used to describe real world situations, e.g. The relationship of the Sonora desert to California, Arizona and Mexico The Sonora desert is the The Sonora desert is part of the CA, AZ and Mexico. sum of CA, AZ and Mexico. Senses of Part

March 15, Senses of In Can the inner object leave by passing between members of the outer group? –Yes -- Try in-Among

March 15, 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

March 15, 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

March 15, Event Types PhysicalStateChangeEvent TemperatureChangingProcess BiologicalDevelopmentEvent ShapeChangeEvent MovementEvent ChangingDeviceState GivingSomething DiscoveryEvent Cracking Carving Buying Thinking Mixing Singing CuttingNails PumpingFluid 11,000 more

March 15, performedBy causes-EventEvent objectPlaced objectOfStateChange outputsCreated inputsDestroyed assistingAgent beneficiary fromLocation toLocation deviceUsed driverActor damages vehicle providerOfMotiveForce transportees Relations Between an Event and its Participants Over 400 more.

March 15, 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

March 15, 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 1 st -order logic

March 15, 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

March 15, 2006 Building Cyc qua Engineering Task amount known rate of learning learning by discovery learning via natural language CYC 750 person-years 21 realtime years $75 million Frontier of human knowledge codify & enter each piece of knowledge, by hand

March 15, (foundingDate AbuSayyaf ?X) AKA by Shallow Fishing Automated Knowledge Acquisition

March 15, 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

March 15, 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

March 15, 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

March 15, (maritalStatus MohamedAtta?X) PersonTypeByMaritalStatus AKA by Shallow Fishing

March 15, (maritalStatus MohamedAtta Single) (maritalStatus MohamedAtta Married) (maritalStatus MohamedAtta Divorced) … (maritalStatus MohamedAtta Cohabitating-Unmarried) (maritalStatus MohamedAtta?X) Generate alternative assertions PersonTypeByMaritalStatus AKA by Shallow Fishing

March 15, (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 Attas fiancee Mohamed Attas wife Mohammed Attas ex-wife …husband, Mohamed Atta,… Generate alternative assertions PersonTypeByMaritalStatus AKA by Shallow Fishing

March 15, (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 Attas fiancee Mohamed Attas wife Mohammed Attas ex-wife …husband, Mohamed Atta,… Generate alternative assertions PersonTypeByMaritalStatus AKA by Shallow Fishing

March 15, 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 commonsense GAFs/hour Hat worn on: Head Neck Foot Leg

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March 15, 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 sources 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.

March 15, Dr. Douglas B. Lenat, 3721 Executive Center Drive, Suite 100, Austin, TX Phone: (512) 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.