logics for data and knowledge representation

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Logics for Data and Knowledge Representation Introduction to Algebra Chiara Ghidini, Luciano Serafini, Fausto Giunchiglia and Vincenzo Maltese

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Logics for Data and Knowledge Representation. Introduction to Algebra. Chiara Ghidini, Luciano Serafini, Fausto Giunchiglia and Vincenzo Maltese. Roadmap. Set theory Basic notions Operations Properties Relations Functions. Describing the world. individuals. Cita. Monkey. sets. - PowerPoint PPT Presentation

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Page 1: Logics for Data and Knowledge Representation

Logics for Data and KnowledgeRepresentation

Introduction to Algebra

Chiara Ghidini, Luciano Serafini, Fausto Giunchiglia and Vincenzo Maltese

Page 2: Logics for Data and Knowledge Representation

Roadmap Set theory

Basic notions Operations Properties

Relations Functions

2

Page 3: Logics for Data and Knowledge Representation

Describing the world

3

Kimba Simba

Cita

Hunts Eats

Monkey

LionNear

individualssetsrelations

Page 4: Logics for Data and Knowledge Representation

Sets A set is a collection of elements The description of a set must be unambiguous and unique: it

must be possible to decide whether an element belongs to the set or not.

4

1 35

7 9

The set of odd numbers < 10

The set of students in this

room

The set of lions in a certain zoo

SETS :: RELATIONS :: FUNCTIONS

Page 5: Logics for Data and Knowledge Representation

Describing sets Listing: the set is described by

listing all its elements

Abstraction: the set is described through a common property of its elements

Venn Diagrams: graphical representation that supports the formal description

5

1 35

7 9

A = {1, 3, 5, 7, 9}

A = { x | x is an odd number < 10}

A

SETS :: RELATIONS :: FUNCTIONS

Page 6: Logics for Data and Knowledge Representation

Basic notions on sets Empty Set: the set with no elements;

A = { } A =

Membership: element a belongs to the set A;

A = {a, b, c} a A

Non membership: element a doesn't belong to the set A

A = {b, c} a A

Equality: the sets A and B contain the same elements;

A = {b, c}; B = {b, c} A = B

6

SETS :: RELATIONS :: FUNCTIONS

Page 7: Logics for Data and Knowledge Representation

Basic notions on sets (cont.) Inequality: the sets A and B contain the same elements;

A = {c}; B = {b, c} A ≠ B

Subset: all elements of A belong to B;

A = {c}; B = {b, c} A B

Proper subset: all elements of A belong to B and they are not the same

A B and A ≠ B then A B

Power set: the set of all the subsets of A A = {a, b} P(A) = {, {a}, {b}, {a, b}}

|A| = n then |P(A)| = 2n

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SETS :: RELATIONS :: FUNCTIONS

Page 8: Logics for Data and Knowledge Representation

Operations on sets Union: the set containing the the

members of A or B

Intersection: the set containing the members of both A and B

8

A B

a

bc d

A B

a

bc d

A B

A B

SETS :: RELATIONS :: FUNCTIONS

Page 9: Logics for Data and Knowledge Representation

Operations on sets (cont.) Difference: the set containing the

members of A and not of B

Complement: given a universal set U, the complement of A is the set whose members are the members of U - A.

9

A B

a

bd

A - Bc

A

_ A

U

SETS :: RELATIONS :: FUNCTIONS

Page 10: Logics for Data and Knowledge Representation

Exercises Given A = {t, z} and B = {v, z, t}, say whether the following

statements are true or false: A B A B z A B v B {v} B v A - B

Given A = {a, b, c, d} and B = {c, d, f} Find a set X such that A B = B X. Is this set unique? Is there any set Y such that A Y = B ?

10

SETS :: RELATIONS :: FUNCTIONS

Page 11: Logics for Data and Knowledge Representation

Properties of sets A A = A A A = A A = A = A

A B = B A A B = B A (commutative)

(A B) C = A (B C)

(A B) C = A (B C) (associative)

A (B C) = (A B) (A C)

A (B C) = (A B) (A C) (distributive)

_____ _ _ A B = A B

_____ _ _

A B = A B (De Morgan laws)11

SETS :: RELATIONS :: FUNCTIONS

Page 12: Logics for Data and Knowledge Representation

Cartesian product Cartesian product of A and B: the set of ordered couples (a, b)

where a is a member of A and b a member of B

A x B = {(a, b) : a A and b B}

Notice that A x B ≠ B x A

Example:

A = {a, b, c}, B = {s, t}

A x B = {(a, s), (a, t), (b, s), (b, t), (c, s), (c, t)}

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SETS :: RELATIONS :: FUNCTIONS

Page 13: Logics for Data and Knowledge Representation

Relations A (binary) relation R from set A to set B is a subset of A x B

R A x B xRy indicates that (x, y) R

The domain of R is the set Dom(R) = {a A | b ∃ B s.t. aRb}

The co-domain of R is the set Cod(R) = {b B | a ∃ A s.t. aRb}

13

bB

a

(a,b) ∈ R

A

SETS :: RELATIONS :: FUNCTIONS

Page 14: Logics for Data and Knowledge Representation

Relations (cont.) An n-ary relation Rn is a subset of A1 x … x An

n is the arity of the relation

The inverse relation of R A x B is the relation R-1 B x A where:

R-1 = {(b, a) | (a, b) R}

14

bB

a

(b, a) ∈ R-1

A

SETS :: RELATIONS :: FUNCTIONS

Page 15: Logics for Data and Knowledge Representation

Properties of relationsLet R be a binary relation on A, i.e. R A x A. R is said to be:

reflexive iff aRa a ∀ A;symmetric iff aRb implies bRa a, b ∀ A;transitive iff aRb and bRc imply aRc a, b, c ∀ A;anti-symmetric iff aRb and bRa imply a = b a, b ∀ A;

15

SETS :: RELATIONS :: FUNCTIONS

Page 16: Logics for Data and Knowledge Representation

Equivalence relations Given R A x A, R is an equivalence relation iff it is reflexive,

symmetric and transitive.

A partition of a set A is a family F of non-empty subsets of A s.t.: the subsets are pairwise disjoint the union of all the subsets is the set A

Notice that each element of A belongs to exactly one subset in F.

Given ≡ equivalence relation on A and a A, the equivalence class of a is the set [a] = {x | a ≡ x}

Notice that if x [a] then [x] = [a]

The quotient set of A w.r.t. ≡ is the set {[x] | x A} which defines a partition of A.

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SETS :: RELATIONS :: FUNCTIONS

Page 17: Logics for Data and Knowledge Representation

Order relations Given R A x A, R is a (partial) order relation iff it is reflexive,

anti-symmetric and transitive.

If the relation holds a, b ∀ A then it is a total order

If a, b ∀ A either aRb or bRa or a = b then it is a strict order

17

SETS :: RELATIONS :: FUNCTIONS

Page 18: Logics for Data and Knowledge Representation

Functions A function f from A to B is a binary relation that associates to

each element a in A exactly one element b in B.

f : A B

The image of an element a A is denoted with f(a) B

Notice that it can be the case that the same element in B is the image of several elements in A.

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SETS :: RELATIONS :: FUNCTIONS

Page 19: Logics for Data and Knowledge Representation

Functions (cont.) f: A B is injective if for distinct elements in A there is a distinct

element in B:

∀ a, b A and a ≠ b then f(a) ≠ f(b)

f: A B is surjective if for each element in B there is at least one element in A:

∀ b B a ∃ A s.t. f(a) = b

f: A B is bijective if it is injective and surjective.

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SETS :: RELATIONS :: FUNCTIONS

Page 20: Logics for Data and Knowledge Representation

Functions (cont.) If f: A B is bijective we can define its inverse function f-1: B A

Given two functions f: A B and g: B C, the composition of f and g is the function g ○ f : C such that:

g ○ f = {(a, g(f(a)) | a A}

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SETS :: RELATIONS :: FUNCTIONS