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Transcript of March 15, 20061 Dr. Douglas B. Lenat, 3721 Executive Center Drive, Suite 100, Austin, TX 78731...
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March 15, 2006
1
Dr. Douglas B. Lenat , 3721 Executive Center Drive, Suite 100, Austin, TX 78731
Email: [email protected]
Phone: (512) 342-4001
2 July 2005
Applications of the CycCyc Formal Ontology
Upper Ontology Symposium
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March 15, 2006
22 July 2005
The sentences are written in logic, not English, so computers can deeply understand them, not just store them. It can deduce the same sorts of things from them that you or I could.
A formal ontology has two parts:
(1) A set of terms (sort of like words)
(2) A set of axioms involving those terms (sort of like sentences built out of them)
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March 15, 2006
32 July 2005
A formal ontology: terms + axioms (in logic)
CYC: 300k terms, 3.2 million handcrafted axioms.Very general ones (“Upper Ontology”) all the way
down to some domain-specific terms and axioms.
This afternoon’s talk: Formal Ontologies in general
This talk: Examples of how CYC is applied today
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Application#1: Smarter searching
• Query: “Someone smiling”
• Caption: “A man helping his daughter take her first step”
find information
find information
by inference (+KB)
by inference (+KB)
When you become happy, you smile.
You become happy when someone you love accomplishes a milestone.
Taking one’s first step is a milestone.
Parents love their children.
(implies (and (isa ?PARENT Person) (children ?PARENT ?CHILD))
(loves ?PARENT ?CHILD))
.
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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
as fa
st a
s us
ual
a se
cond
late
r
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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)
(ex-serviceman OR ”mili
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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)
vets: 25,947 matchesvets: 388,109 matches
as fa
st a
s us
ual
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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
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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 OR vet
AND NOT (ex-serviceman OR "military veteran" OR veteran)
veterinarian OR “veteri
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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 veterinary OR vet
AND NOT (ex-serviceman OR "military veteran" OR veteran)
veterinarian OR “veteri
vets: 153,060 matches
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11
Three Improvements to Search
• Deep semantic search involving n axioms (slow)• Add in OR and AND-NOT terms, to reduce the
number of false negatives and false positives• Suggest plausible appropriate follow-on queries
– For veterinarians: how to train to be a vet
– For veterans: benefits of reenlisting
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Application#2: Deep Question-Answering
• Even 2-3 step reasoning is relatively deep
• Draw on knowledge from all levels of the Cyc ontology (upper, middle, and domain-specific)
• The following examples come from current DTO and AFRL programs transitioned to RDEC
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What factors argue <for/against> the conclusion that <ETA> <performed> <the March 2004 Madrid attacks>?
For:- ETA often executes attacks near national election- ETA has performed multi-target coordinated attacks- Over the past 30 years, ETA performed 75% of all terrorist attacks in Spain- Over the past 30 years, 98% of all terrorist attacks in Spain were performed by Spain-based groups, and ETA is a Spain-based group.
Against:-ETA warns (a few minutes ahead of time) of attacks that would result in a high number civilian casualties, to prevent them. There was no such warning prior to this attack.-ETA generally takes responsibility for its attacks, and it did not do so this time.-ETA has never been known to falsely deny responsibility for an attack, and it did deny responsibility for this attack.
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murder of rafik hariri
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murder of rafik hariri
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CycCyc ReasoningModules
ReasoningModules
Interface to External Data Sources
Interface to External Data Sources
Cyc
API
Cyc
API
Know
led
ge
Entr
y T
ools
Know
led
ge
Entr
y T
ools
User Interface(with Natural Language Dialog)
User Interface(with Natural Language Dialog)
DataBases
WebPages
Text Sources
Other KBs
OtherApplications
OtherApplications
KnowledgeAuthors
KnowledgeAuthors
KnowledgeUsers
KnowledgeUsers
ExternalData
Sources
ExternalData
Sources
Cyc Ontology & Knowledge Base
A Typical Architecture: Formal Ontology + Inference Engines + Interfaces/API’s
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Application#3: Semantic Data Base Integration (Virtual Joins)
• Similar to that last “deep question answering” application, but some of the information is outside the KB: in data bases, on websites, in other ontologies / knowledge bases, etc.
• Map the schema of each of information source to Cyc, and have it call on those external sources as needed, to solve sub-sub-…-problems of the query
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OFAC DB8 USGS NARCL
FBI Most
WantedCATS CDE DB4
DB4
Qusay Hussein
Uday Hussein
SuspN
DB8Prenom
Qusai Hussein 30
Odai Hussein
Surnom ann
Dec. 31, 1996
Sept. 9, 2003YOB
1964
Data Warehousing: a Quadratic Solution
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you! HAL CYC
QusayHusseinAl-Takriti
UdaiHusseinAl-Takriti
(age ?PERSON (YearsDuration ?AGE))
(birthDate ?PERSON ?BIRTH-DATE)
RULES
CONCEPTS
DB4YOB
Qusay Hussein
Uday Hussein 1964
DB8Prenom ann
Qusai Hussein 30
Odai Hussein
OFAC DB8 USGS NARCL
FBI Most
WantedCATS CDE DB4
Dec. 31, 1996
Sept. 9, 2003SuspN
Surnom
1966
32
A Solution that Scales Linearly
(…and, by the way, enables DB population/enrichment)
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DB4YOB
Qusay Hussein
Uday Hussein 1964
DB8Prenom ann
Qusai Hussein 30
Odai Hussein
OFAC DB8 USGS NARCL
FBI Most
WantedCATS CDE DB4
Dec. 31, 1996
Sept. 9, 2003SuspN
Surnom
1966
32
(…and, by the way, enables DB population/enrichment)
A Solution that Scales Linearly
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A very recent Cyc SKSI example
“What major US cities are particularly vulnerable to an anthrax attack?”
The answer is logically implied by data dispersed through several sources:
USGSGNISDB
AMVAKB
RAND R
UNFAODB
DTRACATS
DB
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“major US city” ?C is a U.S. City with >1M population
“particularly vulnerable to an anthrax attack” – the current ambient temperature at ?C is above freezing,
and– ?C has more than 100 people for each hospital bed,
and– the number of anthrax host animals near ?C exceeds 100k
“What major US cities are particularly vulnerable to an anthrax attack?”
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“What major US cities are particularly vulnerable to an anthrax attack?”
U.S. cities with population > 1 million
USGSGNISDB
AMVAKB
RAND R
UNFAODB
DTRACATS
DB
1-2 conjuncts in a
CycL “Ask” expression
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1-2 conjuncts in a
CycL “Ask” expression
“What major US cities are particularly vulnerable to an anthrax attack?”
U.S. cities with population > 1 million
USGSGNISDB
AMVAKB
RAND R
UNFAODB
DTRACATS
DB
(and (isa ?C USCity) (> (NumberOfInhabitantsFn ?C) 106) (vulnerableToScriptedEventTypeUsing ?C DeployingABioAgentByInfectingAZoonoticHost
Anthrax-Bacterium))
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The Geographic Names Information System (GNIS)
DB maintained by the US Geological Survey (USGS).
USGSGNISDB
state | name | type | county | state_fips | -------+-----------------------+-------+----------------+------------+ TX | Dallas | ppl | Dallas | 48 | MN | Hennepin County | civil | Hennepin | 27 | CA | Sacramento County | civil | Sacramento | 6 | AZ | Phoenix | ppl | Maricopa | 4 |
primary_lat | primary_long| elevation | population | status | ------------+-------------+-----------+------------+------------------+ 32.78333 | -96.8 | 463 | 1022830 | BGN 1978 1959 45.01667 | -93.45 | 0 | 1032431 | 38.46667 | -121.31667 | 0 | 1041219 | 33.44833 | -112.07333 | 1072 | 1048949 | BGN 1931 1900 1897
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The Geographic Names Information System (GNIS)
DB maintained by the US Geological Survey (USGS).
USGSGNISDB
So how do we explain to our system that:
• row 1 of that table is “about” the city of Dallas, TX
• the population field of that table contains the numberof inhabitants of the city that that row is “about”
• here is exactly how to access tuples of that database
• that access will be fast, accurate, recent, complete
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The Geographic Names Information System (GNIS)
DB maintained by the US Geological Survey (USGS).
USGSGNISDB
• the population field of that table contains the numberof inhabitants of the city that that row is “about”
We provide the field encodings and decodings, some of which correspond to explicit fields like population, two-letter state codes, etc:
(fieldDecoding Usgs-Gnis-LS ?x (TheFieldCalled “population”) (numberOfInhabitants
(TheReferentOfTheRow Usgs-Gnis) ?x))
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The Geographic Names Information System (GNIS)
DB maintained by the US Geological Survey (USGS).
USGSGNISDB
• how to access tuples of that database We provide all the information needed for a JDBC connection script:
We assert, in the context (MappingMtFn Usgs-KS), all of these:
(passwordForSKS Usgs-KS "geografy")(portNumberForSKS Usgs-KS 4032)(serverOfSKS Usgs-KS "sksi.cyc.com")(sqlProgramForSKS Usgs-KS PostgreSQL)(structuredKnowledgeSourceName Usgs-KS "usgs")(subProtocolForSKS Usgs-KS "postgresql")(userNameForSKS "sksi")
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The Geographic Names Information System (GNIS)
DB maintained by the US Geological Survey (USGS).
USGSGNISDB
• that access will be fast, accurate, recent, complete We provide meta-level assertions about the database, about each table of the database, about the completeness etc. of various kinds of data in the DB, etc.
We assert, in the context (MappingMtFn Usgs-KS):
(schemaCompleteExtentKnownForValueTypeInArg Usgs-Gnis-LSUSCitynumberOfInhabitants 1)
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The Geographic Names Information System (GNIS)
DB maintained by the US Geological Survey (USGS).
USGSGNISDB
• that access will be fast, accurate, recent, complete We provide meta-level assertions about the database, about each table of the database, about the completeness etc. of various kinds of data in the DB, etc.
We assert, in the context (MappingMtFn Usgs-KS):
(resultSetCardinality Usgs-Gnis-PS (TheSet (PhysicalFieldFn Usgs-Gnis-PS "state")) TheEmptySet 60.0)
(resultSetCardinality Usgs-Gnis-PS (TheSet (PhysicalFieldFn Usgs-Gnis-PS "primary_long") (PhysicalFieldFn Usgs-Gnis-PS "primary_lat") (PhysicalFieldFn Usgs-Gnis-PS "name")) (TheSet (PhysicalFieldFn Usgs-Gnis-PS "county") (PhysicalFieldFn Usgs-Gnis-PS "state")) 530.36)
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“major US city” U.S. City with >1M population
“particularly vulnerable to an anthrax attack” – the current ambient temperature at ?C is above freezing,
and– ?C has more than 100 people for each hospital bed,
and– the number of anthrax host animals near ?C exceeds 100k
“What major US cities are particularly vulnerable to an anthrax attack?”
Cyc knows that pullets are chickens, so don’t add those two numbers together!
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A formal ontology: terms + axioms (in logic)
CYC: 300k terms, 3.2 million handcrafted axioms.Very general ones (“Upper Ontology”) all the way
down to some domain-specific terms and axioms.
Three of the CurrentApplications of Cyc:• Smarter searching (augment queries with OR and AND-NOT
terms; suggest meaningful follow-up queries)• Relatively deep Question-answering for analysts• Semantic Knowledge Source Integration (SKSI): map
external DBs, websites, ontologies,… to Cyc for it to call on• 100’s more (that we know of) OpenCyc/ResearchCyc apps.Research: Characterize our systems as agents, to interoperate