chapter 5 knowledge representation
DESCRIPTION
Chapter 5 Knowledge Representation. ID: 106 Name: Yue Lu CS267 Fall 2008 Instructor: Dr. T.Y.Lin. Contents. Introduction Example Formal Definition Significance of Attributes Discernibility Matrix. Introduction. Issue of knowledge representation in the framework of concepts - PowerPoint PPT PresentationTRANSCRIPT
Chapter 5Knowledge Representation
ID: 106Name: Yue LuCS267 Fall 2008Instructor: Dr. T.Y.Lin
Contents
Introduction Example Formal Definition Significance of Attributes Discernibility Matrix
Introduction
Issue of knowledge representation in the framework of concepts
Tabular representation of knowledge represent equivalence relations
Such a table will be called Knowledge Representation System (KRS)
Knowledge Representation System (KRS)
KRS can be viewed as a data table Columns are labeled by attributes Rows are labeled by objects
Each attribute we associate an equivalence relation
Each table can be viewed as a notation for a certain family of equivalence relations
Example of KRS
UA1A2A3A4A5A6A7
SizeSmallMediumLargeSmallMediumLargeLarge
AnimalityBearBearDogCat HorseHorseHorse
ColorBlackBlackBrownBlackBlackBlackBrown
Formal Definition Knowledge Representation System is a pair
S=(U,A) U - is a nonempty, finite set called the universe A - is a nonempty, finite set of primitive
attributes Every primitive attribute a ∈ A is a total
function a : U → Va is the set of values of a, called the domain of a
With every subset of attributes B ⊆ A, we associate a binary relation IND(B), called an indiscernibilty relation and defined thus:
IND(B)={(x, y)∈ U2 :for every a ∈ B, a(x)=a(y)}
UA1A2A3A4A5A6A7
SizeSmallMediumLargeSmallMediumLargeLarge
AnimalityBearBearDogCat HorseHorseHorse
ColorBlackBlackBrownBlackBlackBlackBrown
U = {A1, A2, A3, A4, A5, A6, A7} A = {size, animality, color} V = { (small, medium, large), (bear,
dog, cat, horse), (black, brown) }
UA1A2A3A4A5A6A7
SizeSmallMediumLargeSmallMediumLargeLarge
AnimalityBearBearDogCat HorseHorseHorse
ColorBlackBlackBrownBlackBlackBlackBrown
IND (size) = { (A1, A4), (A2, A5), (A3, A6, A7)}
IND (animality) = { (A1, A2), (A3), (A4), (A5, A6, A7) }
IND (color) = { (A1, A2, A4, A5, A6), (A3, A7) }
UA1A2A3A4A5A6A7
SizeSmallMediumLargeSmallMediumLargeLarge
AnimalityBearBearDogCat HorseHorseHorse
ColorBlackBlackBrownBlackBlackBlackBrown
IND (size, animality) = { (A1), (A2), (A3), (A4), (A5), (A6, A7) }
IND (size, color) = { (A1, A4), (A2, A5), (A3, A7), (A6) }
IND (animality, color) = {(A1, A2), (A3), (A4), (A5, A6), (A7) }
UA1A2A3A4A5A6A7
SizeSmallMediumLargeSmallMediumLargeLarge
AnimalityBearBearDogCat HorseHorseHorse
ColorBlackBlackBrownBlackBlackBlackBrown
IND (size, animality, color) = { (A1), (A2), (A3), (A4), (A5), (A6), (A7) }
U = {1,2,3,4,5,6,7,8} A = {a, b, c} V = {0, 1, 2}
U12345678
a10211220
b0101 0211
c21002011
U/IND(a)= {(1,4,5), (2,8), (3,6,7)} U/IND(b)= {(1,3,5),(2,4,7,8),(6)} U/IND(c)= {(1,5),(2,7,8),(3,4,6)}
U12345678
a10211220
b0101 0211
c21002011
U/IND(a)= {(1,4,5), (2,8), (3,6,7)} U/IND(b)= {(1,3,5),(2,4,7,8),(6)} U/IND(c)= {(1,5),(2,7,8),(3,4,6)}
U12345678
a10211220
b0101 0211
c21002011
U/IND(a)= {(1,4,5), (2,8), (3,6,7)} U/IND(b)= {(1,3,5),(2,4,7,8),(6)} U/IND(c)= {(1,5),(2,7,8),(3,4,6)}
U12345678
a10211220
b0101 0211
c21002011
U/IND(c)= {(1,5),(2,7,8),(3,4,6)} U/IND(a,b) = {(1,5),(2,8),(3),(4),(6),(7)} U/IND(a,b,c) = U/IND(a,b) IND(a,b) ⊂ IND(c); {a,b} => {c} CORE(A) = {a,b}; REDUCT(A) = {a,b}
U12345678
a10211220
b0101 0211
c21002011
Significance of Attributes
KRS is different from relational table
emphasis not on data structuring and manipulation, but on analysis of dependencies in the data
Closer to the statistical data model
Discernibility Matrix
S = (U, A), U={X1, X2, …, Xn} A discernibility matrix of S is a
symmetric n × n matrix with entries Cij = {a ∈ A | a(xi) ≠ a(xj)} for i, j =
1,…,n CORE(A) = {a ∈ A : Cij=(a), for
some i,j }
Example
U a b c d
1 0 1 2 0
2 1 2 0 2
3 1 0 1 0
4 2 1 0 1
5 1 1 0 2
5 ×5 matrix
A={a,b,c,d} CORE(A)={b}
1 2 3 4 5
1 ∅
2 a,b,c,d
∅
3 a,b,c b,c,d ∅
4 a,c,d a,b,d a,b,c,d
∅
5 a,c,d b b,c,d a,d ∅
U a b c d
1 0 1 2 0
2 1 2 0 2
3 1 0 1 0
4 2 1 0 1
5 1 1 0 2
Conclusion
Representing Knowledge using data table Columns are labelled with attributes Rows with object of the universe
With each group of columns we associate an equivalence relation
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