Upper Ontology Symposium Formal Ontologies: Dr. Douglas B. Lenat , 3721 Executive Center Drive, Suite 100, Austin, TX 78731 Email: Lenat@cyc. com Phone: (512) 342 -4001 March 15, 2006 2 July 2005 1
Upper Ontology Symposium Formal Ontologies: Can Anything With That Title be Understandable or Interesting? Dr. Douglas B. Lenat , 3721 Executive Center Drive, Suite 100, Austin, TX 78731 Email: Lenat@cyc. com Phone: (512) 342 -4001 March 15, 2006 2 July 2005 2
Upper Ontology Symposium Formal Ontologies: 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. 2 July 2005 3 March 15, 2006
Contrast: With NO Formal Ontology… … programs are lucky to even have the appearance of understanding, a thin veneer of intelligence. E. g. , ELIZA. The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer queries, search, translate, transcribe speech, etc. 2 July 2005 4 March 15, 2006
ELIZA (DOCTOR) 1965 Joe Weizenbaum, MIT Ca rl Simulation/Parody of reflection psychotherapy Ro ge 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. ” 5 March 15, 2006 rs
ELIZA (DOCTOR) “blah mother blah” Patient: “My dog’s mother died recently. ” “Tell me more about your mother. ” 6 March 15, 2006
ELIZA (DOCTOR) “blah mother blah” Patient: “My dog’s mother died recently. ” “Tell me more about your mother. ” 7 March 15, 2006
ELIZA (DOCTOR) “blah mother blah” Patient: “My dog’s mother died recently. ” “Tell me more about your mother. ” 8 March 15, 2006
ELIZA (DOCTOR) “blah mother blah” Patient: “My dog’s mother died recently. ” “Tell me more about your mother. ” 9 March 15, 2006
ELIZA (DOCTOR) “blah mother blah” Patient: “My dog’s mother died recently. ” “Tell me more about your mother. ” 10 March 15, 2006
ELIZA (DOCTOR) “blah mother blah” Patient: “My dog’s mother died recently. ” “Tell me more about your mother. ” 11 March 15, 2006
ELIZA (DOCTOR) “blah mother blah” Patient: “My dog’s mother died recently. ” “Tell me more about your mother. ” 12 March 15, 2006
![ALICE (today!!) • JUDGE 11[9: 26: 0]I WILL ASK YOU SOME SIMPLE QUESTIONS AND](https://present5.com/presentation/e2c990488d51c51ec254f647c9a58707/image-13.jpg "ALICE (today!!) • JUDGE 11[9: 26: 0]I WILL ASK YOU SOME SIMPLE QUESTIONS AND")
ALICE (today!!) • JUDGE 11[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? • JUDGE 11[9: 26: 16]WHAT IS THE COLOR OF A BLUE CAR • PROGRAM[9: 26: 16]WHAT COLOR IS a blue car • JUDGE 11[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. 13 March 15, 2006
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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 “Which first-run them, how to sources to go to, how to accessmovies combine the intermediateborn in Texas star a teenager results. • Doand arebe able to limit, ahead a theater want to showing today at of time, the uncertainty, recency, granularity, ideology… < 10 minutes’ drive from this building? ” (and/or see such meta-level info for each answer) 16 March 15, 2006
Microwave. Oven is a type of Kitchen-Appliance Dishwasher is a type of Kitchen-Appliance The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. March 15, 2006 2 July 2005 17
Rthagide-disjaks is a type of Kitchen-Appliance Gracinimumples is a type of Kitchen-Appliance The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. March 15, 2006 2 July 2005 18
Rthagide-disjaks is a type of Kitchen-Appliance Gracinimumples is a type of Kitchen-Appliance Rthagide-disjaks requires Electricity. Gracinimumples requires Electricity and Water. The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. March 15, 2006 2 July 2005 19
Rthagide-disjaks is a type of Kitchen-Appliance Gracinimumples is a type of Kitchen-Appliance Rthagide-disjaks requires Vorawnistz. Gracinimumples requires Vorawnistz and Buzqa. The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. March 15, 2006 2 July 2005 20
Rthagide-disjaks is a type of Kitchen-Appliance Gracinimumples is a type of Kitchen-Appliance Rthagide-disjaks requires Vorawnistz. Gracinimumples requires Vorawnistz and Buzqa is a Liquid and supplied through Pipes. The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. March 15, 2006 2 July 2005 21
Rthagide-disjaks is a type of Kitchen-Appliance Gracinimumples is a type of Kitchen-Appliance Rthagide-disjaks requires Vorawnistz. Gracinimumples requires Vorawnistz and Buzqa is a Thwarn and supplied through Epluns. The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. March 15, 2006 2 July 2005 22
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. ) The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. March 15, 2006 2 July 2005 23
Example: Google How could it be more powerful if it were formal (had some understanding)? The basic idea: Get the computer to understand, not just store, information. Then it can reason to answer your queries. March 15, 2006 2 July 2005 24
How formalized knowledge helps search Query: “Show me pictures of strong and adventurous people” ion KB) at (+ orm ce nf en d i fer fin in by Caption: “A man climbing a rock face” 25 March 15, 2006
How formalized knowledge helps search Query: “Outdoor explosions in terrorist events Lebanon between 1990 and 2001” ion KB) at (+ orm ce Document: “ 1993 pipe nf en d i fer bombing on the patio of the fin in Beirut Hilton coffee shop. ” by Text Document 26 March 15, 2006
both general and domain knowledge How formalized knowledge helps search ^ Query: “Threats to low -flying US airliners in Lebanon” ion KB) at (+ orm ce nf en d i fer fin in Document: “Hizballah buys by ten SA-7’s. ” Text Document 27 March 15, 2006
vets Do you mean: • vets (military veteran) • vets (veterinary surgeon) Web Results New Search 1 -25 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 ”mili Do you mean: n vetera ” • vets (military veteran) OR doctor R vet al ran") O “anim • vetsry vete on" R ta (veterinary surgeon) O i e R "mil ry surg a O veterin ceman i " x-serv an OR i (e Web Results ar 1 -25 terin (ve T D NO Revise New Search AN 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 ”mili Do you mean: n vetera ” • vets (military veteran) OR doctor R vet al ran") O “anim • vetsry vete on" R ta (veterinary surgeon) O i e R "mil ry surg a O veterin ceman i " x-serv an OR i (e Web Results ar 1 -25 terin (ve T D NO Revise New Search AN vets: 25, 947 matches 388, 109 matches 1. Veterans News and Information Service - Military, Army, Navy, Marine Corps, Air Force, Coast Guard 2. Surf Point - Society & Issues: Military/Armed Forces: War Veterans 3. A Vet Remembers Retail and Wholesale Merchants of Military/ Veteran Goods and Services 4.
vets Do you mean: • vets (military veteran) • vets (veterinary surgeon) Web Results New Search 1 -25 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 “veteri ” doctor al “anim ) Do you mean: eon" OR eteran v n" OR ry surg a ra veterinvets (militaryaveteran) ry vete " • "milit an OR i R terinar man O • e vets (veterinary surgeon) ve -servic OT (ex N AND Web Results New Search 1 -25 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 “veteri ” doctor al “anim ) Do you mean: eon" OR eteran v n" OR ry surg a ra veterinvets (militaryaveteran) ry vete " • "milit an OR i R terinar man O • e vets (veterinary surgeon) ve -servic OT (ex N AND Web Results New Search 1 -25 Revise vets: 25, 947 matches 153, 060 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
Find and clean (consistency-check) information by inference (+KB) 34 March 15, 2006
Find and clean (consistency-check) information by inference (+KB) 35 March 15, 2006
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? 36 March 15, 2006
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. 37 March 15, 2006
Natural Language Understanding requires having lots of knowledge 3. Mary and Sue are sisters. Mary and Sue are mothers. 38 March 15, 2006
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! 39 March 15, 2006
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!” 40 March 15, 2006 Took Table Sanction. . .
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 41 March 15, 2006
Organize Terms into an Ontology Vehicle Water Vehicle Submarine Vehicle Surface Water Vehicle Tracked Vehicle Overland Vehicle Wheeled Vehicle Truck Railed Vehicle INSTANCE Truck#809726543 42 March 15, 2006
Attach Facts/Rules/. . . to the Nodes (inherit knowledge through class hierarchy) Vehicle Driven by a trained adult human Water Vehicle Surface Vehicle Can’t control its altitude Submarine Vehicle Surface Water Vehicle Tracked Vehicle Overland Vehicle Wheeled Vehicle Truck Leaves tracks Railed Vehicle INSTANCE Truck#809726543 43 March 15, 2006
Move each rule to the best place Vehicle Driven by a trained adult human Water Vehicle Surface Vehicle Can’t control its altitude Submarine Vehicle Surface Water Vehicle Overland Vehicle Slow down in bad weather Tracked Vehicle Wheeled Vehicle Truck Leaves tracks Railed Vehicle INSTANCE Truck#809726543 44 March 15, 2006
Move each rule to the best place Vehicle Driven by a trained adult human Water Vehicle Surface Vehicle Can’t control its altitude Submarine Vehicle Surface Water Vehicle Overland Vehicle Slow down in bad weather Tracked Vehicle Wheeled Vehicle Truck Leaves tracks Railed Vehicle INSTANCE Truck#809726543 45 March 15, 2006
Move each rule to the best place Vehicle Driven by a trained adult human Water Vehicle Surface Vehicle Can’t control its altitude Submarine Vehicle Surface Water Vehicle Overland Vehicle Slow down in bad weather Tracked Vehicle Wheeled Vehicle Truck Leaves tracks Railed Vehicle INSTANCE Truck#809726543 46 March 15, 2006
What do we mean “represent it in logic”? (isa Socrates Man) “Socrates is a man” (genls Man Mortal) “all men are mortal” (For. All ? x (implies (isa ? x Man) (isa ? x Mortal))) “all men are mortal” (For. All ? x “each person has a mother who’s a female person” (implies (isa ? x Person) (There. Exists ? y (and (isa ? y Female. Person) (mother ? x ? y))))) 47 March 15, 2006
What do we mean “it can reason”? Simple: (isa Socrates Man) (For. All ? x (implies (isa ? x Man) (isa ? x Mortal))) Harder: Using general and specific knowledge: Can a can-can? Very complex: An example from our AKB (Analyst’s Knowledge Base) 48 March 15, 2006
What do we mean “it can reason”? Simple: (isa Socrates Man) (For. All ? x (implies (isa ? x Man) (isa ? x Mortal))) (isa Socrates Mortal) Harder: Using general and specific knowledge: Can a can-can? Very complex: An example from our AKB (Analyst’s Knowledge Base) 49 March 15, 2006
Can a can-can? 50 March 15, 2006
Can a can-can? 51 March 15, 2006
What do we mean “it can reason”? Simple: (isa Socrates Man) (For. All ? x (implies (isa ? x Man) (isa ? x Mortal))) (isa Socrates Mortal) Harder: Using general and specific knowledge: Can a can-can? Very complex: An example from our AKB (Analyst’s Knowledge Base) 52 March 15, 2006
The Analyst’s Knowledge Base CT Analyst “Were there any attacks on targets of symbolic value to Muslims since 1987 on a Christian holy day? " Domain Experts "What sequences of events could lead to the destruction of Hoover Dam? " Query Formulation Formulator Explanation Generator Cycorp Tools For: Ontology-Building, -Browsing, -Editing, & Fact/Rule Entry Scenario Generation Generator Reasoning Modules Others’/GOTS Analysis and Collaboration Components General Terrorism Knowledge Terrorism Knowledge Base) Base AKB OWL & Relational DB “projection” of the AKB Interface to Data Repositories HUMINT Messages INS SIGINT Data Message Content Geopolitical Border Data Crossings Global HID Observa Terrain tions Data Weather Travel Records Data March 15, 2006 Credit Satellite Card Intel Records Military 53 Intel output of COTS Text Extraction Systems
( Logically and Arithmetically Combining n Pieces of Info. An example: an analyst’s query posed as part of HPKB (1996) that Cyc answered. ) Information from multiple sources Knowledge about the domain in general Commonsense knowledge about the real world 54 March 15, 2006
There is no one correct monolithic ontology. E. g. , Cyc’s 3 M axioms are divided into thousands of contexts by: granularity, topic, culture, geospatial place, time, . . . There is a correct monolithic reasoning mechanism, but it is so deadly slow that we never call on it unless we have to 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. 55 March 15, 2006
Even though they are expressed in formal logic, most axioms state usuals, not absolute truths. 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). Syria was behind the assassination of Rafik Hariri. Each person had a mother who was also a person. 56 March 15, 2006
Cyc: A Large Formal Ontology Cyc contains: 15, 000 Predicates 300, 000 Concepts 3, 200, 000 Assertions Intangible Individual Thing Sets Relations Space Physical Objects Living Things Ecology Natural Geography Political Geography Weather Earth & Solar System Paths Actors Actions Movement State Change Dynamics Plans Goals Physical Agents Plants Human Anatomy & Physiology Temporal Thing Partially Tangible Thing Logic Math Borders Geometry Animals Emotion Human Products Conceptual Perception Behavior & Devices Works Belief Actions Vehicles Buildings Weapons Spatial Thing Spatial Paths Materials Parts Statics Life Forms Human Beings Human Artifacts Represented in: • First Order Logic • Higher Order Logic Time • Context Logic Events Scripts • Micro-theories Agents Artifacts Thing Mechanical Software Social & Electrical Literature Language Relations, Devices Works of Art Culture Organizational Actions Organizational Plans Agent Organizations Social Behavior Organization Social Activities Human Activities Business & Commerce Purchasing Shopping Types of Organizations Politics Warfare Sports Recreation Entertainment Transportation & Logistics Human Organizations Nations Governments Geo-Politics Professions Occupations Travel Communication Law Everyday Living Business, Military Organizations General Knowledge about Various Domains Specific data, facts, and observations March 15, 2006 57
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Temporal Relations 37 Relations Between Temporal Things temporal. Bounds. Intersect temporal. Bounds. Contain temporally. Intersects temporal. Bounds. Identical starts. After. Starting. Of starts. During ends. After. Ending. Of overlaps. Start starting. Date starting. Point temporally. Contains simultaneous. With temporally. Cooriginating after 59 March 15, 2006
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 60 March 15, 2006
Senses of ‘Part’ parts intangible. Parts sub. Information sub. Events physical. Decompositions physical. Portions physical. Parts external. Parts internal. Parts anatomical. Parts constituents functional. Part 61 March 15, 2006
Senses of ‘Part’ Concepts in mereotopology: X is part of Y X overlaps Y X is connected to Y X is is the sum the objects Y 1…Yn 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 part of the sum of CA, AZ and Mexico. 62 March 15, 2006
23 Senses of ‘In’ • Can the inner object leave by passing between members of the outer group? – Yes -- Try in-Among 63 March 15, 2006
23 Senses of ‘In’ • Does part of the inner object stick out of the container? – If the container were turned around could the contained object fall out? – None of it. -- Try in-Cont. Completely Yes -- Try in-Cont. Open – Yes -- Try in-Cont. Partially No -- Try in-Cont. Closed 64 March 15, 2006
23 Senses of ‘In’ Is it attached to the inside of the outer object? – Yes -- Try connected. To. Inside Can it be removed, if enough force is used, without damaging either object? – Yes -- Try in-Snugly or screwed. In Does the inner object stick into the outer object? Yes -- Try sticks. Into 65 March 15, 2006
Event Types Cracking Physical. State. Change. Event Carving Temperature. Changing. Proc Buying ess Thinking Biological. Development. Eve Mixing nt Singing Shape. Change. Event Cutting. Nails Movement. Event Pumping. Fluid Changing. Device. State Giving. Something Discovery. Event 11, 000 more 66 March 15, 2006
Relations Between an Event and its Participants performed. By causes-Event object. Placed object. Of. State. Change outputs. Created inputs. Destroyed assisting. Agent beneficiary from. Location to. Location device. Used driver. Actor damages vehicle provider. Of. Motive. Fo rce transportees Over 400 more. 67 March 15, 2006
Emotion • Types of Emotions: – – – Adulation Abhorrence Relaxed-Feeling Gratitude Anticipation-Feeling • Predicates For Defining and Attributing Emotions: – – – contrary. Feelings appropriate. Emotion action. Expresses. Feeling feels. Towards. Object feels. Towards. Person. Type – Over 120 of these 68 March 15, 2006
Propositional Attitudes Relations Between Agents and Propositions • • • goals intends desires hopes expects beliefs • • • opinions knows remembered. Prop perceives. That sees. That tastes. That Most of these are modal and assertions using them go beyond 1 st-order logic 69 March 15, 2006
Devices • Over 4000 Specializations of Physical. Device Specific Predicates • – Clothes. Washer – Nuclear. Aircraft. Carrier • • Vocabulary for Describing Device Functions – primary. Function-Device. Type gun. Caliber speed. Of Device States (40+) Device. On Cocked. State 70 March 15, 2006
rate of learning 1984 2006 Building Cyc qua Engineering Task g via learnin nguage tural la na e ov ry c ng rni b is yd lea codify & enter each piece of knowledge, by hand CYC amount known n-years 7 o 50 pers e years tim 21 real llion $75 mi Fro nti er of hu ma nk no wle dg e
Automated Knowledge Acquisition AKA by Shallow Fishing (founding. Date Abu. Sayyaf ? X) 72 March 15, 2006
Automated Knowledge Acquisition AKA by Shallow Fishing • Abu Sayyaf was founded in ___ • Al Harakat Islamiya, established in ___ • ASG was established in ___ Search Strings (founding. Date Abu. Sayyaf ? X) 73 March 15, 2006
Automated Knowledge Acquisition AKA by Shallow Fishing • Abu Sayyaf was founded in ___ • Al Harakat Islamiya, established in ___ • ASG was established in ___ Search Strings (founding. Date Abu. Sayyaf ? X) 74 March 15, 2006
Automated Knowledge Acquisition AKA by Shallow Fishing • Abu Sayyaf was founded in ___ • Al Harakat Islamiya, established in ___ • ASG was established in ___ Search Strings (founding. Date Abu. Sayyaf ? X) Abu Sayyaf was founded in the early 1990 s Parse (founding. Date Abu. Sayyaf (Early. Part. Fn (Decade. Fn 199))) 75 March 15, 2006
AKA by Shallow Fishing (marital. Status Mohamed. Atta? X) Person. Type. By. Marital. Status 76 March 15, 2006
AKA by Shallow Fishing • (marital. Status Mohamed. Atta Single) • (marital. Status Mohamed. Atta Married) • (marital. Status Mohamed. Atta Divorced) … • (marital. Status Mohamed. Atta Cohabitating. Unmarried) Generate alternative assertions (marital. Status Mohamed. Atta? X) Person. Type. By. Marital. Status 77 March 15, 2006
AKA by Shallow Fishing • (marital. Status Mohamed. Atta Single) • (marital. Status Mohamed. Atta Married) • (marital. Status Mohamed. Atta Divorced) … • (marital. Status Mohamed. Atta Cohabitating. Unmarried) Generate alternative assertions For each one, generate a set of search strings • Mohamed Atta’s fiancee • Mohamed Atta’s wife • Mohammed Atta’s exwife • …husband, Mohamed Atta, … (marital. Status Mohamed. Atta? X) Person. Type. By. Marital. Status 78 March 15, 2006
AKA by Shallow Fishing • (marital. Status Mohamed. Atta Single) • (marital. Status Mohamed. Atta Married) • (marital. Status Mohamed. Atta Divorced) … • (marital. Status Mohamed. Atta Cohabitating. Unmarried) Generate alternative assertions For each one, generate a set of search strings • Mohamed Atta’s fiancee • Mohamed Atta’s wife • Mohammed Atta’s exwife • …husband, Mohamed Atta, … (marital. Status Mohamed. Atta? X) Person. Type. By. Marital. Status (marital. Status Mohamed. Atta Married) 79 March 15, 2006
Harnessing Lots of Users WWW. CYC. COM • 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 useful distinguishing facts Hat worn on: Head Neck Foot Leg 80 March 15, 2006
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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. What Needs to be Shared? • • 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. ) 86 March 15, 2006
I. e. , share a formal ontology, including a full upper ontology, large portions of Upper Ontology Symposium a middle ontology, and relevant slivers of a lower (domain-dependent) ontology. Formal Ontologies: Can Anything With That Title be Understandable or Interesting? Dr. Douglas B. Lenat , 3721 Executive Center Drive, Suite 100, Austin, TX 78731 Email: Lenat@cyc. com Phone: (512) 342 -4001 March 15, 2006 2 July 2005 87
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