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 o v a n n i S i l e n o T é l é c o m P a r i s T e c h , P a r i s - D a u p h i n e U n i v e r s i t y

What is Knowledge? ● K i s w h a t w e a s c r i b e t o a n a g e n t t o p r e d i c t n o w l e d g e h i s b e h a v i o u r u s i n g p r i n c i p l e s o f r a t i o n a l i t y . e x a m p l e o f r a t i o n a l i t y p r i n c i p l e : I f a c o u r s e o f a c t i o n l e a d t o m y g o a l , I w i l l t a k e t h a t c o u r s e o f a c t i o n . N o t e : 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 l y r e p r o d u c e s t h a t w h i c h w e a s c r i b e ; i t i s n o t i n t e n d e d t o b e a c c u r a t e , p h y s i c a l m o d e l N e w e l l , A . ( 1 9 8 2 ) . T h e K n o w 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 .

Data, Information, Knowledge ● D : u n i n t e r p r e t e d s i g n a l s o r s y m b o l s a t a ● I : d a t a w i t h a d d e d m e a n i n g n f o r m a t i o n ● K a l l d a t a a n d i n f o r m a t i o n t h a t p e o p l e u s e n o w l e d g e : t o a c t , a c c o m p l i s h t a s k s a n d t o c r e a t e n e w i n f o r m a t i o n ( k n o w - h o w , - w h y , - w h o , - w h e r e a n d - w h e n ) .

Data, Information, Knowledge : . . . - - - . . . D a t a i t i s a m e s s a g e s a y i n g S O S I n f o r m a t i o n : e m e r g e n c y s i g n a l , s t a r t r e s c u e o p e r a t i o n . K n o w l e d g e : : 0 1 4 5 4 3 1 2 0 0 D a t a i t i s a t e l e p h o n e n u m b e r o f a p e r s o n I n f o r m a t i o n : t o m a k e a n a p p o i n t m e n t I n e e d t o c a l l i t K n o w l e d g e :

Types of knowledge ● I o f t e n n o t c o n s c i o u s , i n t e r n a l m p l i c i t , ● E , c o n s c i o u s a n d e x t e r n a l x p l i c i t ● T , i n c o n t r a s t a c i t t o w h a t i s i n f o c u s ( P o l a n y i ) P i c t u r e f r o m B r o h m , R . ( 2 0 0 7 ) . B r i n g i n g P o l y a n i o n t h e T h e a t r e S t a g e

More types of knowledge ● P p r o c e d u r e s , p l a n s r o c e d u r a l k n o w l e d g e : ● D c o n c e p t s , o b j e c t s , f a c t s e c l a r a t i v e k n o w l e d g e : ● H e x p e r i e n c e , d e f a u l t s e u r i s t i c k n o w l e d g e : ● K p r o b a b i l i t y n o w l e d g e a b o u t u n c e r t a i n t y : e s t i m a t i o n s , d e f a u l t s , k n o w i n g w h a t y o u k n o w ● C g l o b a l c o n c e p t s a n d o m m o n - s e n s e k n o w l e d g e : t h e o r i e s , g e n e r a l h i e r a r c h i e s ● Me k n o w l e d g e a b o u t k n o w l e d g e t y p e s t a - k n o w l e d g e : a n d t h e i r u s e

What is Knowledge Representation? ● s u r r o g a t e a s i m p l i fj e d r e p r e s e n t a t i o n ● e x p r e s s i o n o f o n t o l o g i c a l c o m m i t m e n t r e i f y i n g o u r a t t e n t i o n t o t h e w o r l d ● t h e o r y o f i n t e l l i g e n t r e a s o n i n g a n d a m o d e l o f a s s o c i a t e d r e a s o n i n g p r o c e s s e s ● m e d i u m o f e ffj c i e n t c o m p u t a t i o n t h a t i s a c c e s s i b l e t o p r o g r a m s ● m e d i u m o f h u m a n e x p r e s s i o n a n d t o p e o p l e R . D a v i s , H . S h r o b e , a n d P . S z o l o v i t s . Wh a t i s a K n o w l e d g e R e p r e s e n t a t i o n ? A I M a g a z i n e , 1 4 ( 1 ) : 1 7 - 3 3 , 1 9 9 3 . h t t p : / / g r o u p s . c s a i l . m i t . e d u / m e d g / f t p / p s z / k - r e p . h t m l

Knowledge systems

Example of expert system 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 ?

From expert systems to KBS ● E x p e r t s y s t e m s S e p a r a t e k n o w l e d g e ( r u l e s ) f r o m t h e r e a s o n i n g e n g i n e ● K n o w l e d g e - b a s e d s y s t e m s S e p a r a t e k n o w l e d g e ( c o n c e p t s ) f r o m r u l e s a n d r e a s o n i n g – e x a m p l e : F r a m e s ● s t e r e o t y p e d s t r u c t u r e s o f k n o w l e d g e – e x a m p l e : S e m a n t i c n e t w o r k s ● r e p r e s e n t a t i o n b y g r a p h - b a s e d f o r m a l i s m ● m o d e l e n t i t i e s a n d t h e i r r e l a t i o n s

Frames ● F r a m e s a r e " s t e r e o t y p e d " k n o w l e d g e u n i t s r e p r e s e n t i n g s i t u a t i o n s , o b j e c t s o r e v e n t s o r ( c l a s s e s ) s e t s o f s u c h e n t i t i e s . A f r a m e i s a c o l l e c t i o n o f a t t r i b u t e s ( s l o t s ) , s p e c i fj e d b y f a c e t s t h a t ● c o r r e s p o n d t o t h e v a l u e s t h e y a c q u i r e o r p r o c e d u r e s t h a t l a u n c h .

“ Wi l l y t h r e w a b a l l t o M o r g a n . ”

“ Wi l l y t h r e w a b a l l t o M o r g a n . ” n o d e s : e n t i t i e s a r c s : r e l a t i o n s h i p s

“ Wi l l y t h r e w a b a l l t o M o r g a n . ” n o d e s : l a b e l s o n n o d e s : e n t i t y e n t i t i e s n a m e s l a b e l s o n a r c s : r e l a t i o n a r c s : n a m e s r e l a t i o n s h i p s

“ Wi l l y t h r e w a b a l l t o M o r g a n . ” 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( throwing, X, willy). fact( Act, object, What ). ?- who_action_what(willy, throwing, ball).

We n e e d m o r e k n o w l e d g e t o i n f e r s o m e t h i n g m o r e i n t e r e s t i n g ! “ Wi l l y t h r e w a b a l l t o M o r g a n . ” 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( throwing, X, willy). fact( Act, object, What ). ?- who_action_what(willy, throwing, ball).

Semantic Networks K n o w 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 o g n i t i o n , a i m t o b e r e u s a b l e a n d e ffj c i e n t . . .

Semantic Networks . . . 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 o t w h a t w e r e a d !