程序代写代做代考 database flex Knowledge engineering

Knowledge engineering
KR is first and foremost about knowledge
meaning and entailment
find individuals and properties, then encode facts sufficient for entailments
Before implementing, need to understand clearly • whatistobecomputed?
• whyandwhereinferenceisnecessary?
Example domain: soap-opera world
people, places, companies, births, marriages, divorces, deaths, events, …
Task: KB with appropriate entailments • whatvocabulary?
• whatfactstorepresent?
KR & R © Brachman & Levesque 2005 Expressing Knowledge 1

Vocabulary
Domain-dependent predicates and functions
main question:
what are the individuals?
here: people, places, companies, …
named individuals
john, countryTown, faultyInsuranceCorp, fic, johnQsmith, …
basic types
Person, Place, Man, Woman, …
attributes
Rich, Beautiful, Unscrupulous, …
relationships
LivesAt, MarriedTo, DaughterOf, HairDresserOf, HadAnAffairWith, Blackmails, …
functions
fatherOf, ceoOf, bestFriendOf, …
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Basic facts
Usually atomic sentences and negations
type facts
Man(john),
Woman(jane), Company(faultyInsuranceCorp)
property facts
Rich(john), ¬HappilyMarried(jim), W orksFor(jim,fic)
equality facts
john = ceoOf(fic),
fic = faultyInsuranceCorp, bestFriendOf(jim) = john
Like a simple database
could store these facts in relational tables
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Complex facts
Universal abbreviations
“y[Woman(y) Ù y 1 jane É Loves(y,john)] “y[Rich(y) Ù Man(y) É Loves(y,jane)] “x”y[Loves(x,y) É ¬Blackmails(x,y)]
possible to express without quantifiers Incomplete knowledge
Loves(jane,john) Ú Loves(jane,jim) which?
$x[Adult(x) Ù Blackmails(x,john)] who?
cannot write down more complete version
Closure axioms
“x[Person(x) É x=jane Ú x=john Ú x=jim …] “x”y[MarriedTo(x,y) É … ]
“x[ x=fic Ú x=jane Ú x=john Ú x=jim …]
limits domain of discourse
also useful to have jane 1 john …
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© Brachman & Levesque 2005 Expressing Knowledge 4

Terminological facts
General relationships among predicates. For example:
disjoint
“x[Mammal(x) É ¬Reptile(x)] subtype
“x[Mammal(x) É Animal(x)] exhaustive
“x[Day(x) É Monday(x) Ú … Ú Sunday(x)] symmetry
“x”y [RelatedTo(x,y) É RelatedTo(y,x)] inverse
“x”y [ChildOf(x,y) É ParentOf(y,x)] type restriction
“x”y [MarriedTo(x,y) É Person(x) Ù Person(y)]
full definition
“x[RichMan(x) o Rich(x) Ù Man(x)]
Usually universally quantified conditionals or
biconditionals
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Entailments: 1
Is there a company whose CEO loves Jane?
$x [Company(x) Ù Loves(ceoOf(x),jane)] ??
Suppose I |= KB.
Then I |= Rich(john), Man(john),
and I |= “y[Rich(y)ÙMan(y) É Loves(y,jane)] so I |= Loves(john,jane).
Also I |= john = ceoOf(fic),
so I |= Loves( ceoOf(fic),jane).
Finally I |= Company(faultyInsuranceCorp), and I |= fic = faultyInsuranceCorp,
so I |= Company(fic).
Thus, I |= Company(fic) Ù Loves( ceoOf(fic),jane), and so
I |= $x [Company(x) Ù Loves(ceoOf(x),jane)]. Can extract identity of company from this proof
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Entailments: 2
If no man is blackmailing John, then is he being blackmailed by somebody he loves?
“x[Man(x) É ¬Blackmails(x,john)] É $y[Loves(john,y) Ù Blackmails(y,john)] ??
Note: KB|=(aÉb) iff KBÈ{a}|=b Assume: I |= KB È {“x[Man(x) É ¬Blackmails(x,john)]}
Show: I |= $y[Loves(john,y) Ù Blackmails(y,john)
Have: $x[Adult(x) Ù Blackmails(x,john)] and “x[Adult(x) É Man(x) ÚWoman(x)] so $x[Woman(x) Ù Blackmails(x,john)].
Then: “y[Rich(y) Ù Man(y) É Loves(y,jane)] and Rich(john) Ù Man(john)
so Loves(john,jane)!
But: “y[Woman(y) Ù y 1 jane É Loves(y,john)]
and “x”y[Loves(x,y) É ¬Blackmails(x,y)]
so “y[Woman(y) Ù y 1 jane É ¬Blackmails(y,john)] and… Blackmails(jane,john)
Finally: Loves(john,jane) Ù Blackmails(jane,john) so: $y[Loves(john,y) Ù Blackmails(y,john)]
Proof as sequence of sentences
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What individuals?
Sometimes useful to reduce n-ary predicates to 1-place predicates and 1-place functions
• involvesreifyingproperties:newindividuals
• typicalofdescriptionlogics/framelanguages
Flexibility in terms of arity:
Purchases(john,sears,bike) or Purchases(john,sears,bike,feb14) or Purchases(john,sears,bike,feb14,$100)
Instead introduce purchase objects
Purchase(p) Ù agent(p)=john Ù obj(p)=bike Ù source(p)=sears Ù amount(p)=… Ù…
(later)
allows purchase to be described at various levels of detail Complex relationships:
MarriedTo(x,y) vs. PreviouslyMarriedTo(x,y) vs. ReMarriedTo(x,y)
Define marital status in terms of existence of marriages and divorces.
Marriage(m) Ù partner1(m)=x Ù partner2(m)=y Ù date(m)=… Ù witness(m)=… Ù …
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Abstract individuals
Also need individuals for numbers, dates, times, addresses, etc.
objects about which we ask wh-questions
Quantities as individuals
age(suzy) = 14
age-in-years(suzy) = 14 age-in-months(suzy) = 168
perhaps better to have an object for the age of Suzy, whose value in years is 14
years(age(suzy)) = 14 months(x) = 12*years(x) centimeters(x) = 100*meters(x)
Similarly with locations and times instead of
time(m)=”Jan 5 1992 4:47:03EST” can use
time(m)=t Ù year(t)=1992 Ù …
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Other sorts of facts
Statistical / probabilistic facts
• HalfofthecompaniesarelocatedontheEastSide.
• Mostoftheemployeesarerestless.
• Almostnoneoftheemployeesarecompletelytrustworthy,
Default / prototypical facts
• Companypresidentstypicallyhavesecretariesintercepting their phone calls.
• Carshavefourwheels.
• Companiesgenerallydonotallowemployeesthatwork together to be married.
Intentional facts
• JohnbelievesthatHenryistryingtoblackmailhim.
• JanedoesnotwantJimtothinkthatshelovesJohn.
Others …
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