Logic and Knowledge Representation K n o w l e d g e r e - - PowerPoint PPT Presentation
Logic and Knowledge Representation K n o w l e d g e r e p r e s e n t a t i o n , O n t o l o g i e s , S e m a n t i c We b 2 4 M a y 2 0 1 7 g s i l e n o @e n s t . f r G i
T é l é c
P a r i s T e c h , P a r i s
a u p h i n e U n i v e r s i t y
K n
l e d g e r e p r e s e n t a t i
, O n t
i e s , S e m a n t i c We b
2 4 M a y 2 1 7
N e w e l l , A . ( 1 9 8 2 ) . T h e K n
l e d g e L e v e l . A r t i fj c i a l I n t e l l i g e n c e , 1 8 ( 1 ) , 8 7 – 1 2 7 .
P i c t u r e f r
B r
m , R . ( 2 7 ) . B r i n g i n g P
y a n i
t h e T h e a t r e S t a g e
R . D a v i s , H . S h r
e , a n d P . S z
i t s . Wh a t i s a K n
l e d g e R e p r e s e n t a t i
? A I M a g a z i n e , 1 4 ( 1 ) : 1 7
3 , 1 9 9 3 . h t t p : / / g r
p s . c s a i l . m i t . e d u / m e d g / f t p / p s z / k
e p . h t m l
a s i m p l i fj e d r e p r e s e n t a t i
r e i f y i n g
r a t t e n t i
t
h e w
l d a n d a m
e l
a s s
i a t e d r e a s
i n g p r
e s s e s t h a t i s a c c e s s i b l e t
r
r a m s a n d t
e
l e
if flower and seed then phanerogam if phanerogam and bare-seed then fir if phanerogam and 1-cotyledon then monocotyledonous if phanerogam and 2-cotyledon then dicotyledonous if monocotyledon and rhizome then thrush if dicotyledon then anemone if monocotyledon and ¬rhizome then lilac if leaf and flower then cryptogamous if cryptogamous and ¬root then foam if cryptogamous and root then fern if ¬leaf and plant then thallophyte if thallophyte and chlorophyll then algae if thallophyte and ¬ chlorophyll then fungus if ¬leaf and ¬flower and ¬plant then colibacille
rhizome + flower + seed + 1-cotyledon ?
– e
– e
f r a m e i s a c
l e c t i
a t t r i b u t e s ( s l
s ) , s p e c i fj e d b y f a c e t s t h a t c
r e s p
d t
h e v a l u e s t h e y a c q u i r e
p r
e d u r e s t h a t l a u n c h .
fact( throwing, actor, willy ). % or: throwing( actor, willy ). fact( throwing, receiver, morgan ). % or: throwing( receiver, morgan ). fact( throwing, object, ball ). % or: throwing( object, ball ). who_action_what( Who, Act, What) :- fact( Act, actor, Who ), fact( Act, object, What ). ?- fact( throwing, X, willy). ?- who_action_what(willy, throwing, ball).
fact( throwing, actor, willy ). % or: throwing( actor, willy ). fact( throwing, receiver, morgan ). % or: throwing( receiver, morgan ). fact( throwing, object, ball ). % or: throwing( object, ball ). who_action_what( Who, Act, What) :- fact( Act, actor, Who ), fact( Act, object, What ). ?- fact( throwing, X, willy). ?- who_action_what(willy, throwing, ball).
K n
l e d g e s y s t e m s , i n s p i r e d b y h u m a n c
n i t i
, a i m t
e r e u s a b l e a n d e ffj c i e n t . . .
. . . b u t w h a t t h e m a c h i n e r e a d s i s n
w h a t w e r e a d !
A n n
a t i
p r i n c i p l e : d i fg e r e n c e s i n i n t e n d e d p r
e s s i n g s h
l d b e r e fm e c t e d i n d i fg e r e n c e s i n t h e s y m b
s t r u c t u r e s
P r i m i t i v e c
c e p t s ( * ) d
h a v e s u ffj c i e n t c
d i t i
s , m a y h a v e n e c e s s a r y D e r i v e d c
c e p t s s p e c i fj e d b y s u ffj c i e n t a n d n e c e s s a r y c
d i t i
s .
B r a c h m a n , R . J . , & S c h m
z e , J . ( 1 9 8 5 ) . A n O v e r v i e w
t h e K L
N E K n
l e d g e R e p r e s e n t a t i
S y s t e m . C
n i t i v e S c i e n c e , 9 , 1 7 1 – 2 1 6 .
B r a c h m a n , R . J . , & S c h m
z e , J . ( 1 9 8 5 ) . A n O v e r v i e w
t h e K L
N E K n
l e d g e R e p r e s e n t a t i
S y s t e m . C
n i t i v e S c i e n c e , 9 , 1 7 1 – 2 1 6 . A MESSAGE is a THING with at least one Sender, all of which are PERSONs, at least one Recipient, all of which are PERSONs, exactly one Body, which is a TEXT, exactly one SendDate, which is a DATE, and exactly one ReceivedDate, which is a DATE.
v / r : v a l u e r e s t r i c t i
n / r : n u m b e r r e s t r i c t i
( m i n , m a x ) , N I L = ∞
B r a c h m a n , R . J . , & S c h m
z e , J . ( 1 9 8 5 ) . A n O v e r v i e w
t h e K L
N E K n
l e d g e R e p r e s e n t a t i
S y s t e m . C
n i t i v e S c i e n c e , 9 , 1 7 1 – 2 1 6 . A STARFLEET-MESSAGE is a MESSAGE, all of whose Senders are STARFLEET-COMMANDERS.
r
e r e s t r i c t i
t h r
g h v / r
– t
a k e s a n e w C
c e p t a n d a u t
a t i c a l l y d e t e r m i n e s a l l s u b s u m p t i
r e l a t i
s ( i s
) b e t w e e n i t a n d a l l
h e r C
c e p t s i n t h e n e t w
k
– a
d d s n e w l i n k s w h e n n e w s u b s u m p t i
r e l a t i
s a r e d i s c
e r e d
– a
u t
a t e s t h e p l a c e m e n t
n e w C
c e p t s i n t h e t a x
y
– I
t i s s
n d ( a l l f
n d s u b s u m p t i
r e l a t i
s a r e l e g i t i m a t e ) b u t n
c
p l e t e ( i t d
s n
fj n d a l l s u b s u m p t i
r e l a t i
s )
– c
– n
– o
– n
– q
– p
– o
– n
– a
– i
– l
alive(Elvis). is(Elvis, alive). alive(elvis). alive(Elvis, true). vivant(Elvis).
– r
– p
– u
⊃
– p
– F
– p
– p
– u
Name Profession Birth Elvis Singer 1935 Obama President 1961 … … … Name Resolution Brand Sony T300 4 MP Sony Ixus700 12 MP Canon … … …
P e r s
Name Profession Birth S i n g e r Name Profession Birth Instrument m
e g e n e r a l c l a s s , f e w p r
e r t i e s m
e s p e c i fj c c l a s s , m
e p r
e r t i e s , s
e r e s t r i c t i
s : = s i n g e r i n h e r i t e d p r
e r t i e s a d d i t i
a l p r
e r t i e s r e s t r i c t i
i n h e r i t a n c e / s u b c l a s s r e l a t i
s h i p
elvis_is_person elvis_is_alive elvis_is_dead => ~ elvis_is_alive
=> elvis_is_alive elvis_is_person elvis_is_alive elvis_is_dead => ~ elvis_is_alive elvis_is_dead => michael_jackson_alive => elvis_is_dead
=> elvis_is_alive
elvis_is_person: elvis_is_alive elvis_is_alive => elvis_is_alive elvis_is_dead ~elvis_is_alive if Elvis is a person (and nothing says he’s not alive) then he is alive if Elvis is dead then he is not alive elvis_is_person elvis_is_dead
prerequisite conclusion justification
– h
– r
The Web was designed as an information space, with the goal that it
should be useful not only for human-human communication, but also that machines would be able to participate and help.
One of the major obstacles to this has been the fact that most information on the Web is designed for human consumption, and even if it was derived from a database with well
defined meanings (in at least some terms) for its columns, that the structure of the data is not evident to a robot browsing the Web. Leaving aside the artificial intelligence problem of training machines to behave like people, the Semantic Web approach instead develops
languages for expressing information in a machine processable form.
T i m B e r n e r s
e e , T h e S e m a n t i c We b R
d m a p .
– P
r
l e m s w i t h t h i s a p p r
c h
n fm e x i b l e
i m i t e d n u m b e r
t h i n g s c a n b e e x p r e s s e d
– o
n t
i e s p r
i d e a v
a b u l a r y
t e r m s t h a t a r e m a c h i n e u n d e r s t a n d a b l e
– n
e w t e r m s c a n b e f
m e d b y c
b i n i n g e x i s t i n g
e s a s a k i n d
“ c
c e p t u a l L e g
– m
e a n i n g ( s e m a n t i c s )
s u c h t e r m s i s f
m a l l y s p e c i fj e d
L i n k i n g
e n d a t a c l
d d i a g r a m : h t t p : / / l
l
d . n e t /
– a
– a
– o
– s
d e n t i f y e n t i t i e s ( U R I s )
– e
x p r e s s f a c t s ( R D F )
– e
x p r e s s c
c e p t s ( R D F S )
– s
h a r e v
a b u l a r i e s
– r
e a s
f a c t s ( O WL )
– u
– u
– D
– E
– a
– b
– (
– a
– b
– (
– r
– w
– a
– b
– (
– r
– w
.
– t
– a
– e
– o
– c
– p
– s
– s
– A
– A
– d
– p
– p
– d
– r
(ex:MotorVehicle, rdf:type, rdfs:Class) (ex:PassengerVehicle, rdf:type, rdfs:Class) (ex:Van, rdf:type, rdfs:Class) (ex:Truck, rdf:type, rdfs:Class) (ex:MiniVan, rdf:type, rdfs:Class) (ex:PassengerVehicle, rdfs:subClassOf, ex:MotorVehicle) (ex:Van, rdfs:subClassOf, ex:MotorVehicle) (ex:Truck, rdfs:subClassOf, ex:MotorVehicle) (ex:MiniVan, rdfs:subClassOf, ex:Van) (ex:MiniVan, rdfs:subClassOf, ex:PassengerVehicle)
– W3
– q
– s
PREFIX abc: <http://mynamespace.com/exampleOntology#> SELECT ?capital ?country WHERE { ?x abc:cityname ?capital. ?y abc:countryname ?country. ?x abc:isCapitalOf ?y. ?y abc:isInContinent abc:africa. }
–
c a r d i n a l i t y r e s t r i c t i
s ,
–
l
a l r a n g e c
s t r a i n t s ,
–
e q u a l i t y
r e s
r c e s ,
–
i n v e r s e , s y m m e t r i c a n d t r a n s i t i v e p r
e r t i e s ,
–
b
e a n c l a s s c
b i n a t i
s ,
–
d i s j
n t n e s s a n d c
p l e t e n e s s , …