SlideShare a Scribd company logo
1 of 41
Download to read offline
First Steps in EL Contraction

          Richard Booth             Tommie Meyer          Ivan Jos´ Varzinczak
                                                                  e
      Mahasarakham University                     Meraka Institute, CSIR
             Thailand                             Pretoria, South Africa




Booth, Meyer, Varzinczak (MU/KSG)        EL Contraction                          1 / 24
Outline

 1   Preliminaries
       Belief Change
       Description Logic EL


 2   Contraction in EL
       First Attempt
       A More Fine-grained Approach
       Other Types of Contraction


 3   Conclusion
       Summary, Open questions and Further Work




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   2 / 24
Outline

 1   Preliminaries
       Belief Change
       Description Logic EL


 2   Contraction in EL
       First Attempt
       A More Fine-grained Approach
       Other Types of Contraction


 3   Conclusion
       Summary, Open questions and Further Work




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   2 / 24
Outline

 1   Preliminaries
       Belief Change
       Description Logic EL


 2   Contraction in EL
       First Attempt
       A More Fine-grained Approach
       Other Types of Contraction


 3   Conclusion
       Summary, Open questions and Further Work




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   2 / 24
Outline

 1   Preliminaries
       Belief Change
       Description Logic EL


 2   Contraction in EL
       First Attempt
       A More Fine-grained Approach
       Other Types of Contraction


 3   Conclusion
       Summary, Open questions and Further Work




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   3 / 24
Revision, Expansion and Contraction
       Expansion: K + ϕ
       Revision: K         ϕ, with K   ϕ |= ϕ and K      ϕ |= ⊥
       Contraction: K − ϕ |= ϕ




Booth, Meyer, Varzinczak (MU/KSG)       EL Contraction            4 / 24
Revision, Expansion and Contraction
       Expansion: K + ϕ
       Revision: K         ϕ, with K   ϕ |= ϕ and K      ϕ |= ⊥
       Contraction: K − ϕ |= ϕ

 Also meaningful for ontologies




Booth, Meyer, Varzinczak (MU/KSG)       EL Contraction            4 / 24
AGM Approach
 Contraction described on the knowledge level
 Rationality Postulates
         (K−1) K − ϕ = Cn(K − ϕ)
         (K−2) K − ϕ ⊆ K
         (K−3) If ϕ ∈ K , then K − ϕ = K
                    /
         (K−4) If |= ϕ, then ϕ ∈ K − ϕ
                               /
         (K−5) If ϕ ≡ ψ, then K − ϕ = K − ψ
         (K−6) If ϕ ∈ K , then (K − ϕ) + ϕ = K




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   5 / 24
AGM Approach
 Contraction described on the knowledge level
 Rationality Postulates
         (K−1) K − ϕ = Cn(K − ϕ)
         (K−2) K − ϕ ⊆ K
         (K−3) If ϕ ∈ K , then K − ϕ = K
                    /
         (K−4) If |= ϕ, then ϕ ∈ K − ϕ
                               /
         (K−5) If ϕ ≡ ψ, then K − ϕ = K − ψ
         (K−6) If ϕ ∈ K , then (K − ϕ) + ϕ = K




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   5 / 24
AGM Approach
 Construction method:
       Identify the maximally consistent subsets that do not entail ϕ
       (remainder sets)
       Pick some non-empty subset of remainder sets
              Take their intersection: Partial meet
       Pick all remainder sets: Full meet
       Pick a single remainder set: Maxichoice




Booth, Meyer, Varzinczak (MU/KSG)      EL Contraction                   6 / 24
AGM Approach
 Example
 Contraction of {p → r } from Horn theory K = Cn({p → q, q → r })


                                          p∧r →q           p∧q →r

                                    p→q                    q→r

                                              p→r


       Maxichoice?
       Full meet?




Booth, Meyer, Varzinczak (MU/KSG)         EL Contraction            7 / 24
AGM Approach
 Example
 Contraction of {p → r } from Horn theory K = Cn({p → q, q → r })


                                          p∧r →q           p∧q →r

                                    p→q                    q→r

                                              p→r


                    1                    2
       Maxichoice? Hmc = Cn({p → q}) or Hmc = Cn({q → r , p ∧ r → q})
       Full meet?




Booth, Meyer, Varzinczak (MU/KSG)         EL Contraction                7 / 24
AGM Approach
 Example
 Contraction of {p → r } from Horn theory K = Cn({p → q, q → r })


                                          p∧r →q           p∧q →r

                                    p→q                    q→r

                                              p→r


                    1                    2
       Maxichoice? Hmc = Cn({p → q}) or Hmc = Cn({q → r , p ∧ r → q})
       Full meet? Hfm = Cn({p ∧ r → q})




Booth, Meyer, Varzinczak (MU/KSG)         EL Contraction                7 / 24
Outline

 1   Preliminaries
       Belief Change
       Description Logic EL


 2   Contraction in EL
       First Attempt
       A More Fine-grained Approach
       Other Types of Contraction


 3   Conclusion
       Summary, Open questions and Further Work




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   8 / 24
Description Logic EL [Baader, 2003] with ⊥
       Concepts
                                    C ::= A |      |⊥|C       C | ∃R.C
       Interpretations I = ∆I , ·I

                                      AI ⊆ ∆I , R I ⊆ ∆I × ∆I ,
                                             I   = ∆I , ⊥I = ∅,
                                        (C       D)I = C I ∩ D I ,
                     (∃R.C )I = {a ∈ ∆I | ∃b.(a, b) ∈ R I and b ∈ C I }




Booth, Meyer, Varzinczak (MU/KSG)            EL Contraction               9 / 24
Description Logic EL [Baader, 2003]
       Axioms C           D
                                    I |= C       D iff C I ⊆ D I
       TBox T: set of axioms

                              I |= T iff I satisfies every axiom in T

       T |= C        D iff for all I if I |= T then I |= C         D
       Cn(T) = {C             D | T |= C     D}




Booth, Meyer, Varzinczak (MU/KSG)          EL Contraction             10 / 24
Description Logic EL [Baader, 2003]
 Example
 T = Cn({A           B, B       ∃R.A})


                                    A    ∃R.A       B
                                                           A       B    ∃R.A
                    A     B
                                                               B       ∃R.A

                                         A      ∃R.A




Booth, Meyer, Varzinczak (MU/KSG)         EL Contraction                       11 / 24
Outline

 1   Preliminaries
       Belief Change
       Description Logic EL


 2   Contraction in EL
       First Attempt
       A More Fine-grained Approach
       Other Types of Contraction


 3   Conclusion
       Summary, Open questions and Further Work




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   12 / 24
Motivation
 Let T be a TBox and Φ be a set of axioms
      Contract T with Φ
              we want T |= Φ
              Some axiom in Φ should not follow from T anymore




Booth, Meyer, Varzinczak (MU/KSG)    EL Contraction              13 / 24
Following Delgrande’s Approach [KR’2008]
 Definition (Remainder Sets)
 For a belief set T, X ∈ T ↓ Φ iff
       X ⊆T
       X |= Φ
       for every X s.t. X ⊂ X ⊆ T, X |= Φ.

       We call T ↓ Φ the remainder sets of T w.r.t. Φ
       Do they exist?
              EL is compact and has a Tarskian consequence relation

 Definition (Selection Functions)
 A selection function σ is a function from P(P(LEL )) to P(P(LEL ))
 s.t. σ(T ↓ Φ) = {T} if T ↓ Φ = ∅, and σ(T ↓ Φ) ⊆ T ↓ Φ otherwise.


Booth, Meyer, Varzinczak (MU/KSG)    EL Contraction                   14 / 24
Following Delgrande’s Approach [KR’2008]
 Definition (Remainder Sets)
 For a belief set T, X ∈ T ↓ Φ iff
       X ⊆T
       X |= Φ
       for every X s.t. X ⊂ X ⊆ T, X |= Φ.

       We call T ↓ Φ the remainder sets of T w.r.t. Φ
       Do they exist?
              EL is compact and has a Tarskian consequence relation

 Definition (Selection Functions)
 A selection function σ is a function from P(P(LEL )) to P(P(LEL ))
 s.t. σ(T ↓ Φ) = {T} if T ↓ Φ = ∅, and σ(T ↓ Φ) ⊆ T ↓ Φ otherwise.


Booth, Meyer, Varzinczak (MU/KSG)    EL Contraction                   14 / 24
Following Delgrande’s Approach [KR’2008]
 Definition (Remainder Sets)
 For a belief set T, X ∈ T ↓ Φ iff
       X ⊆T
       X |= Φ
       for every X s.t. X ⊂ X ⊆ T, X |= Φ.

       We call T ↓ Φ the remainder sets of T w.r.t. Φ
       Do they exist?
              EL is compact and has a Tarskian consequence relation

 Definition (Selection Functions)
 A selection function σ is a function from P(P(LEL )) to P(P(LEL ))
 s.t. σ(T ↓ Φ) = {T} if T ↓ Φ = ∅, and σ(T ↓ Φ) ⊆ T ↓ Φ otherwise.


Booth, Meyer, Varzinczak (MU/KSG)    EL Contraction                   14 / 24
Following Delgrande’s Approach
 Definition (Partial Meet Contraction)
 Given a selection function σ, −σ is a partial meet contraction iff
 T −σ Φ = σ(T ↓ Φ).

 Definition (Maxichoice and Full Meet)
 Given a selection function σ, −σ is a maxichoice contraction iff σ(T ↓ Φ) is
 a singleton set. It is a full meet contraction iff σ(T ↓ Φ) = T ↓ Φ when
 T ↓ Φ = ∅.




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                      15 / 24
Following Delgrande’s Approach
 Definition (Partial Meet Contraction)
 Given a selection function σ, −σ is a partial meet contraction iff
 T −σ Φ = σ(T ↓ Φ).

 Definition (Maxichoice and Full Meet)
 Given a selection function σ, −σ is a maxichoice contraction iff σ(T ↓ Φ) is
 a singleton set. It is a full meet contraction iff σ(T ↓ Φ) = T ↓ Φ when
 T ↓ Φ = ∅.




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                      15 / 24
Following Delgrande’s Approach
 Example
 Contraction of {A             ∃R.A} from T = Cn({A           B, B      ∃R.A})


                                    A   ∃R.A       B
                                                          A       B    ∃R.A
                    A     B
                                                              B       ∃R.A

                                        A      ∃R.A

       Maxichoice?
       Full meet?




Booth, Meyer, Varzinczak (MU/KSG)        EL Contraction                          16 / 24
Following Delgrande’s Approach
 Example
 Contraction of {A             ∃R.A} from T = Cn({A           B, B      ∃R.A})


                                    A   ∃R.A       B
                                                          A       B    ∃R.A
                    A     B
                                                              B       ∃R.A

                                        A      ∃R.A

                    1
       Maxichoice? Tmc = Cn({A              2
                                    B}) or Tmc = Cn({B    ∃R.A, A      ∃R.A   B})
       Full meet?




Booth, Meyer, Varzinczak (MU/KSG)        EL Contraction                             16 / 24
Following Delgrande’s Approach
 Example
 Contraction of {A             ∃R.A} from T = Cn({A              B, B      ∃R.A})


                                    A      ∃R.A       B
                                                             A       B    ∃R.A
                    A     B
                                                                 B       ∃R.A

                                           A      ∃R.A

                    1
       Maxichoice? Tmc = Cn({A               2
                                     B}) or Tmc = Cn({B      ∃R.A, A      ∃R.A   B})
       Full meet? Tfm = Cn({A       ∃R.A    B})




Booth, Meyer, Varzinczak (MU/KSG)           EL Contraction                             16 / 24
Following Delgrande’s Approach
 Example
 Contraction of {A             ∃R.A} from T = Cn({A                 B, B      ∃R.A})


                                    A      ∃R.A       B
                                                                A       B    ∃R.A
                    A     B
                                                                    B       ∃R.A

                                           A       ∃R.A

                    1
       Maxichoice? Tmc = Cn({A               2
                                     B}) or Tmc = Cn({B          ∃R.A, A     ∃R.A   B})
       Full meet? Tfm = Cn({A       ∃R.A    B})
       What about T = Cn({A         ∃R.A    B, A    B        ∃R.A})?




Booth, Meyer, Varzinczak (MU/KSG)           EL Contraction                                16 / 24
Following Delgrande’s Approach
 Example
 Contraction of {A             ∃R.A} from T = Cn({A                 B, B      ∃R.A})


                                    A      ∃R.A       B
                                                                A       B    ∃R.A
                    A     B
                                                                    B       ∃R.A

                                           A       ∃R.A

                    1
       Maxichoice? Tmc = Cn({A               2
                                     B}) or Tmc = Cn({B          ∃R.A, A     ∃R.A   B})
       Full meet? Tfm = Cn({A       ∃R.A    B})
       What about T = Cn({A         ∃R.A    B, A    B        ∃R.A})?
                  2
       Tfm ⊆ T ⊆ Tmc , but there is no partial meet contraction yielding T !


Booth, Meyer, Varzinczak (MU/KSG)           EL Contraction                                16 / 24
Outline

 1   Preliminaries
       Belief Change
       Description Logic EL


 2   Contraction in EL
       First Attempt
       A More Fine-grained Approach
       Other Types of Contraction


 3   Conclusion
       Summary, Open questions and Further Work




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   17 / 24
Beyond Partial Meet [Booth et al., IJCAI’09]
 Definition (Infra-Remainder Sets)
 For belief sets T and X , X ∈ T ⇓ Φ iff there is some X ∈ T ↓ Φ s.t.
 ( T ↓ Φ) ⊆ X ⊆ X .

 We call T ⇓ Φ the infra-remainder sets of T w.r.t. Φ.
 Infra-remainder sets contain all belief sets between some remainder set and
 the intersection of all remainder sets




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                      18 / 24
Beyond Partial Meet [Booth et al., IJCAI’09]
 Definition (EL Contraction)
 An infra-selection function τ is a function from P(P(LEL )) to P(LEL )
 s.t. τ (T ⇓ Φ) = T whenever |= Φ, and τ (T ⇓ Φ) ∈ T ⇓ Φ otherwise. A
 contraction function −τ is an EL-contraction iff T −τ Φ = τ (T ⇓ Φ).




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                   19 / 24
A Representation Result
 Basic postulates for EL contraction
        (T − 1) T − Φ = Cn(T − Φ)
        (T − 2) T − Φ ⊆ T
        (T − 3) If Φ ⊆ T then T − Φ = T
        (T − 4) If |= Φ then Φ ⊆ T − Φ
        (T − 5) If Cn(Φ) = Cn(Ψ) then T − Φ = T − Ψ
        (T − 6) If ϕ ∈ T  (T − Φ) then there is a T such that
                   (T ↓ Φ) ⊆ T ⊆ T, T |= Φ, and T + {ϕ} |= Φ

 Conjecture
 Every EL contraction satisfies (T − 1)–(T − 6). Conversely, every
 contraction function satisfying (T − 1)–(T − 6) is an EL contraction.




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                       20 / 24
A Representation Result
 Basic postulates for EL contraction
        (T − 1) T − Φ = Cn(T − Φ)
        (T − 2) T − Φ ⊆ T
        (T − 3) If Φ ⊆ T then T − Φ = T
        (T − 4) If |= Φ then Φ ⊆ T − Φ
        (T − 5) If Cn(Φ) = Cn(Ψ) then T − Φ = T − Ψ
        (T − 6) If ϕ ∈ T  (T − Φ) then there is a T such that
                   (T ↓ Φ) ⊆ T ⊆ T, T |= Φ, and T + {ϕ} |= Φ

 Conjecture
 Every EL contraction satisfies (T − 1)–(T − 6). Conversely, every
 contraction function satisfying (T − 1)–(T − 6) is an EL contraction.




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                       20 / 24
Outline

 1   Preliminaries
       Belief Change
       Description Logic EL


 2   Contraction in EL
       First Attempt
       A More Fine-grained Approach
       Other Types of Contraction


 3   Conclusion
       Summary, Open questions and Further Work




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   21 / 24
Other Types of Contraction
 Inconsistency-based Contraction [Delgrande, KR’2008]
 Let T be a TBox and Φ be a set of axioms
       Contract T ‘making room’ for Φ
       We want T + Φ |= ⊥

 Package Contraction [Booth et al., IJCAI’09]
 Let T be a TBox and Φ be a set of axioms
       Contract T so that none of the axioms in Φ follows from it
       Removal of all sentences in Φ from T




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                  22 / 24
Other Types of Contraction
 Inconsistency-based Contraction [Delgrande, KR’2008]
 Let T be a TBox and Φ be a set of axioms
       Contract T ‘making room’ for Φ
       We want T + Φ |= ⊥

 Package Contraction [Booth et al., IJCAI’09]
 Let T be a TBox and Φ be a set of axioms
       Contract T so that none of the axioms in Φ follows from it
       Removal of all sentences in Φ from T




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                  22 / 24
Outline

 1   Preliminaries
       Belief Change
       Description Logic EL


 2   Contraction in EL
       First Attempt
       A More Fine-grained Approach
       Other Types of Contraction


 3   Conclusion
       Summary, Open questions and Further Work




Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction   23 / 24
Summary, Open questions and Further Work
 Summary
       Basic AGM account of contraction for EL
       Weaker than partial meet contraction
 Open questions
       Are infra-remainder sets enough?
       Is Cn(.) what we really want?
       Kernel contraction? (Renata knows the answer         )
       What about the syntax? (A, A         ∃R.A, A   ∃R.∃R.A,. . . )
 Current and Future Work
       Answer questions above
       Full AGM setting: extended postulates
       Relation to justifications in ontology repair



Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                      24 / 24
Summary, Open questions and Further Work
 Summary
       Basic AGM account of contraction for EL
       Weaker than partial meet contraction
 Open questions
       Are infra-remainder sets enough?
       Is Cn(.) what we really want?
       Kernel contraction? (Renata knows the answer         )
       What about the syntax? (A, A         ∃R.A, A   ∃R.∃R.A,. . . )
 Current and Future Work
       Answer questions above
       Full AGM setting: extended postulates
       Relation to justifications in ontology repair



Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                      24 / 24
Summary, Open questions and Further Work
 Summary
       Basic AGM account of contraction for EL
       Weaker than partial meet contraction
 Open questions
       Are infra-remainder sets enough?
       Is Cn(.) what we really want?
       Kernel contraction? (Renata knows the answer         )
       What about the syntax? (A, A         ∃R.A, A   ∃R.∃R.A,. . . )
 Current and Future Work
       Answer questions above
       Full AGM setting: extended postulates
       Relation to justifications in ontology repair



Booth, Meyer, Varzinczak (MU/KSG)   EL Contraction                      24 / 24

More Related Content

What's hot

On Decomposition of gr* - closed set in Topological Spaces
	On Decomposition of gr* - closed set in Topological Spaces	On Decomposition of gr* - closed set in Topological Spaces
On Decomposition of gr* - closed set in Topological Spacesinventionjournals
 
Cd32939943
Cd32939943Cd32939943
Cd32939943IJMER
 
Seminar on Motivic Hall Algebras
Seminar on Motivic Hall AlgebrasSeminar on Motivic Hall Algebras
Seminar on Motivic Hall AlgebrasHeinrich Hartmann
 
A Review Article on Fixed Point Theory and Its Application
A Review Article on Fixed Point Theory and Its ApplicationA Review Article on Fixed Point Theory and Its Application
A Review Article on Fixed Point Theory and Its Applicationijtsrd
 
11.coupled fixed point theorems in partially ordered metric space
11.coupled fixed point theorems in partially ordered metric space11.coupled fixed point theorems in partially ordered metric space
11.coupled fixed point theorems in partially ordered metric spaceAlexander Decker
 
On best one sided approximation by multivariate lagrange
      On best one sided approximation by multivariate lagrange      On best one sided approximation by multivariate lagrange
On best one sided approximation by multivariate lagrangeAlexander Decker
 
The Fundamental theorem of calculus
The Fundamental theorem of calculus The Fundamental theorem of calculus
The Fundamental theorem of calculus AhsanIrshad8
 
FUZZY IDEALS AND FUZZY DOT IDEALS ON BH-ALGEBRAS
FUZZY IDEALS AND FUZZY DOT IDEALS ON BH-ALGEBRASFUZZY IDEALS AND FUZZY DOT IDEALS ON BH-ALGEBRAS
FUZZY IDEALS AND FUZZY DOT IDEALS ON BH-ALGEBRASIAEME Publication
 
[SEMINAR] 2nd Tues, 14 May, 2019
[SEMINAR] 2nd Tues, 14 May, 2019[SEMINAR] 2nd Tues, 14 May, 2019
[SEMINAR] 2nd Tues, 14 May, 2019Naoto Agawa
 
A Note on the Generalization of the Mean Value Theorem
A Note on the Generalization of the Mean Value TheoremA Note on the Generalization of the Mean Value Theorem
A Note on the Generalization of the Mean Value Theoremijtsrd
 
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...IOSR Journals
 
IRJET- On Semigroup and its Connections with Lattices
IRJET- On Semigroup and its Connections with LatticesIRJET- On Semigroup and its Connections with Lattices
IRJET- On Semigroup and its Connections with LatticesIRJET Journal
 
sarminIJMA1-4-2015 forth paper after been publishing
sarminIJMA1-4-2015 forth paper after been publishingsarminIJMA1-4-2015 forth paper after been publishing
sarminIJMA1-4-2015 forth paper after been publishingMustafa El-sanfaz
 
On combination and conflict - Belief function school lecture
On combination and conflict - Belief function school lectureOn combination and conflict - Belief function school lecture
On combination and conflict - Belief function school lectureSebastien Destercke
 
Decomposition of continuity and separation axioms via lower and upper approxi...
Decomposition of continuity and separation axioms via lower and upper approxi...Decomposition of continuity and separation axioms via lower and upper approxi...
Decomposition of continuity and separation axioms via lower and upper approxi...Alexander Decker
 
11.numerical solution of fuzzy hybrid differential equation by third order ru...
11.numerical solution of fuzzy hybrid differential equation by third order ru...11.numerical solution of fuzzy hybrid differential equation by third order ru...
11.numerical solution of fuzzy hybrid differential equation by third order ru...Alexander Decker
 

What's hot (20)

On Decomposition of gr* - closed set in Topological Spaces
	On Decomposition of gr* - closed set in Topological Spaces	On Decomposition of gr* - closed set in Topological Spaces
On Decomposition of gr* - closed set in Topological Spaces
 
Cd32939943
Cd32939943Cd32939943
Cd32939943
 
Seminar on Motivic Hall Algebras
Seminar on Motivic Hall AlgebrasSeminar on Motivic Hall Algebras
Seminar on Motivic Hall Algebras
 
Ab4101165167
Ab4101165167Ab4101165167
Ab4101165167
 
A Review Article on Fixed Point Theory and Its Application
A Review Article on Fixed Point Theory and Its ApplicationA Review Article on Fixed Point Theory and Its Application
A Review Article on Fixed Point Theory and Its Application
 
11.coupled fixed point theorems in partially ordered metric space
11.coupled fixed point theorems in partially ordered metric space11.coupled fixed point theorems in partially ordered metric space
11.coupled fixed point theorems in partially ordered metric space
 
4
44
4
 
On best one sided approximation by multivariate lagrange
      On best one sided approximation by multivariate lagrange      On best one sided approximation by multivariate lagrange
On best one sided approximation by multivariate lagrange
 
The Fundamental theorem of calculus
The Fundamental theorem of calculus The Fundamental theorem of calculus
The Fundamental theorem of calculus
 
FUZZY IDEALS AND FUZZY DOT IDEALS ON BH-ALGEBRAS
FUZZY IDEALS AND FUZZY DOT IDEALS ON BH-ALGEBRASFUZZY IDEALS AND FUZZY DOT IDEALS ON BH-ALGEBRAS
FUZZY IDEALS AND FUZZY DOT IDEALS ON BH-ALGEBRAS
 
AI Lesson 13
AI Lesson 13AI Lesson 13
AI Lesson 13
 
[SEMINAR] 2nd Tues, 14 May, 2019
[SEMINAR] 2nd Tues, 14 May, 2019[SEMINAR] 2nd Tues, 14 May, 2019
[SEMINAR] 2nd Tues, 14 May, 2019
 
A Note on the Generalization of the Mean Value Theorem
A Note on the Generalization of the Mean Value TheoremA Note on the Generalization of the Mean Value Theorem
A Note on the Generalization of the Mean Value Theorem
 
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...
On Some Types of Fuzzy Separation Axioms in Fuzzy Topological Space on Fuzzy ...
 
AI Lesson 16
AI Lesson 16AI Lesson 16
AI Lesson 16
 
IRJET- On Semigroup and its Connections with Lattices
IRJET- On Semigroup and its Connections with LatticesIRJET- On Semigroup and its Connections with Lattices
IRJET- On Semigroup and its Connections with Lattices
 
sarminIJMA1-4-2015 forth paper after been publishing
sarminIJMA1-4-2015 forth paper after been publishingsarminIJMA1-4-2015 forth paper after been publishing
sarminIJMA1-4-2015 forth paper after been publishing
 
On combination and conflict - Belief function school lecture
On combination and conflict - Belief function school lectureOn combination and conflict - Belief function school lecture
On combination and conflict - Belief function school lecture
 
Decomposition of continuity and separation axioms via lower and upper approxi...
Decomposition of continuity and separation axioms via lower and upper approxi...Decomposition of continuity and separation axioms via lower and upper approxi...
Decomposition of continuity and separation axioms via lower and upper approxi...
 
11.numerical solution of fuzzy hybrid differential equation by third order ru...
11.numerical solution of fuzzy hybrid differential equation by third order ru...11.numerical solution of fuzzy hybrid differential equation by third order ru...
11.numerical solution of fuzzy hybrid differential equation by third order ru...
 

Viewers also liked

A Modularity Approach for a Fragment of ALC
A Modularity Approach for a Fragment of ALCA Modularity Approach for a Fragment of ALC
A Modularity Approach for a Fragment of ALCIvan Varzinczak
 
Pertinence Construed Modally
Pertinence Construed ModallyPertinence Construed Modally
Pertinence Construed ModallyIvan Varzinczak
 
Semantic Diff as the Basis for Knowledge Base Versioning
Semantic Diff as the Basis for Knowledge Base VersioningSemantic Diff as the Basis for Knowledge Base Versioning
Semantic Diff as the Basis for Knowledge Base VersioningIvan Varzinczak
 
On Action Theory Change: Semantics for Contraction and its Properties
On Action Theory Change: Semantics for Contraction and its PropertiesOn Action Theory Change: Semantics for Contraction and its Properties
On Action Theory Change: Semantics for Contraction and its PropertiesIvan Varzinczak
 
10 Insightful Quotes On Designing A Better Customer Experience
10 Insightful Quotes On Designing A Better Customer Experience10 Insightful Quotes On Designing A Better Customer Experience
10 Insightful Quotes On Designing A Better Customer ExperienceYuan Wang
 
How to Build a Dynamic Social Media Plan
How to Build a Dynamic Social Media PlanHow to Build a Dynamic Social Media Plan
How to Build a Dynamic Social Media PlanPost Planner
 

Viewers also liked (7)

A Modularity Approach for a Fragment of ALC
A Modularity Approach for a Fragment of ALCA Modularity Approach for a Fragment of ALC
A Modularity Approach for a Fragment of ALC
 
Proceedings of ARCOE'09
Proceedings of ARCOE'09Proceedings of ARCOE'09
Proceedings of ARCOE'09
 
Pertinence Construed Modally
Pertinence Construed ModallyPertinence Construed Modally
Pertinence Construed Modally
 
Semantic Diff as the Basis for Knowledge Base Versioning
Semantic Diff as the Basis for Knowledge Base VersioningSemantic Diff as the Basis for Knowledge Base Versioning
Semantic Diff as the Basis for Knowledge Base Versioning
 
On Action Theory Change: Semantics for Contraction and its Properties
On Action Theory Change: Semantics for Contraction and its PropertiesOn Action Theory Change: Semantics for Contraction and its Properties
On Action Theory Change: Semantics for Contraction and its Properties
 
10 Insightful Quotes On Designing A Better Customer Experience
10 Insightful Quotes On Designing A Better Customer Experience10 Insightful Quotes On Designing A Better Customer Experience
10 Insightful Quotes On Designing A Better Customer Experience
 
How to Build a Dynamic Social Media Plan
How to Build a Dynamic Social Media PlanHow to Build a Dynamic Social Media Plan
How to Build a Dynamic Social Media Plan
 

Similar to First Steps in EL Contraction

Next Steps in Propositional Horn Contraction
Next Steps in Propositional Horn ContractionNext Steps in Propositional Horn Contraction
Next Steps in Propositional Horn ContractionIvan Varzinczak
 
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...VjekoslavKovac1
 
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastavaBIOLOGICAL FORUM
 
Spectral sum rules for conformal field theories
Spectral sum rules for conformal field theoriesSpectral sum rules for conformal field theories
Spectral sum rules for conformal field theoriesSubham Dutta Chowdhury
 
Testing for mixtures at BNP 13
Testing for mixtures at BNP 13Testing for mixtures at BNP 13
Testing for mixtures at BNP 13Christian Robert
 
Cusps of the Kähler moduli space and stability conditions on K3 surfaces
Cusps of the Kähler moduli space and stability conditions on K3 surfacesCusps of the Kähler moduli space and stability conditions on K3 surfaces
Cusps of the Kähler moduli space and stability conditions on K3 surfacesHeinrich Hartmann
 
Constraints on conformal field theories in d=3
Constraints on conformal field theories in d=3Constraints on conformal field theories in d=3
Constraints on conformal field theories in d=3Subham Dutta Chowdhury
 
International Journal of Mathematics and Statistics Invention (IJMSI)
International Journal of Mathematics and Statistics Invention (IJMSI) International Journal of Mathematics and Statistics Invention (IJMSI)
International Journal of Mathematics and Statistics Invention (IJMSI) inventionjournals
 
Fixed point theorem in fuzzy metric space with e.a property
Fixed point theorem in fuzzy metric space with e.a propertyFixed point theorem in fuzzy metric space with e.a property
Fixed point theorem in fuzzy metric space with e.a propertyAlexander Decker
 
Justification of canonical quantization of Josephson effect in various physic...
Justification of canonical quantization of Josephson effect in various physic...Justification of canonical quantization of Josephson effect in various physic...
Justification of canonical quantization of Josephson effect in various physic...Krzysztof Pomorski
 
Conformal field theories and three point functions
Conformal field theories and three point functionsConformal field theories and three point functions
Conformal field theories and three point functionsSubham Dutta Chowdhury
 
THEME – 2 On Normalizing Transformations of the Coefficient of Variation for a ...
THEME – 2 On Normalizing Transformations of the Coefficient of Variation for a ...THEME – 2 On Normalizing Transformations of the Coefficient of Variation for a ...
THEME – 2 On Normalizing Transformations of the Coefficient of Variation for a ...ICARDA
 
dhirota_hone_corrected
dhirota_hone_correcteddhirota_hone_corrected
dhirota_hone_correctedAndy Hone
 
Tutorial on EM algorithm – Part 3
Tutorial on EM algorithm – Part 3Tutorial on EM algorithm – Part 3
Tutorial on EM algorithm – Part 3Loc Nguyen
 
NCE, GANs & VAEs (and maybe BAC)
NCE, GANs & VAEs (and maybe BAC)NCE, GANs & VAEs (and maybe BAC)
NCE, GANs & VAEs (and maybe BAC)Christian Robert
 
How many components in a mixture?
How many components in a mixture?How many components in a mixture?
How many components in a mixture?Christian Robert
 
Computational Tools and Techniques for Numerical Macro-Financial Modeling
Computational Tools and Techniques for Numerical Macro-Financial ModelingComputational Tools and Techniques for Numerical Macro-Financial Modeling
Computational Tools and Techniques for Numerical Macro-Financial ModelingVictor Zhorin
 
Spectral properties of the Goldstino in supersymmetric Bose-Fermi mixtures
Spectral properties of the Goldstino in supersymmetric Bose-Fermi mixturesSpectral properties of the Goldstino in supersymmetric Bose-Fermi mixtures
Spectral properties of the Goldstino in supersymmetric Bose-Fermi mixturesDaisuke Satow
 
A common random fixed point theorem for rational inequality in hilbert space
A common random fixed point theorem for rational inequality in hilbert spaceA common random fixed point theorem for rational inequality in hilbert space
A common random fixed point theorem for rational inequality in hilbert spaceAlexander Decker
 

Similar to First Steps in EL Contraction (20)

Next Steps in Propositional Horn Contraction
Next Steps in Propositional Horn ContractionNext Steps in Propositional Horn Contraction
Next Steps in Propositional Horn Contraction
 
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...
 
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava
8 fixed point theorem in complete fuzzy metric space 8 megha shrivastava
 
Spectral sum rules for conformal field theories
Spectral sum rules for conformal field theoriesSpectral sum rules for conformal field theories
Spectral sum rules for conformal field theories
 
Testing for mixtures at BNP 13
Testing for mixtures at BNP 13Testing for mixtures at BNP 13
Testing for mixtures at BNP 13
 
Cusps of the Kähler moduli space and stability conditions on K3 surfaces
Cusps of the Kähler moduli space and stability conditions on K3 surfacesCusps of the Kähler moduli space and stability conditions on K3 surfaces
Cusps of the Kähler moduli space and stability conditions on K3 surfaces
 
Constraints on conformal field theories in d=3
Constraints on conformal field theories in d=3Constraints on conformal field theories in d=3
Constraints on conformal field theories in d=3
 
International Journal of Mathematics and Statistics Invention (IJMSI)
International Journal of Mathematics and Statistics Invention (IJMSI) International Journal of Mathematics and Statistics Invention (IJMSI)
International Journal of Mathematics and Statistics Invention (IJMSI)
 
Fixed point theorem in fuzzy metric space with e.a property
Fixed point theorem in fuzzy metric space with e.a propertyFixed point theorem in fuzzy metric space with e.a property
Fixed point theorem in fuzzy metric space with e.a property
 
Justification of canonical quantization of Josephson effect in various physic...
Justification of canonical quantization of Josephson effect in various physic...Justification of canonical quantization of Josephson effect in various physic...
Justification of canonical quantization of Josephson effect in various physic...
 
Conformal field theories and three point functions
Conformal field theories and three point functionsConformal field theories and three point functions
Conformal field theories and three point functions
 
THEME – 2 On Normalizing Transformations of the Coefficient of Variation for a ...
THEME – 2 On Normalizing Transformations of the Coefficient of Variation for a ...THEME – 2 On Normalizing Transformations of the Coefficient of Variation for a ...
THEME – 2 On Normalizing Transformations of the Coefficient of Variation for a ...
 
dhirota_hone_corrected
dhirota_hone_correcteddhirota_hone_corrected
dhirota_hone_corrected
 
Tutorial on EM algorithm – Part 3
Tutorial on EM algorithm – Part 3Tutorial on EM algorithm – Part 3
Tutorial on EM algorithm – Part 3
 
NCE, GANs & VAEs (and maybe BAC)
NCE, GANs & VAEs (and maybe BAC)NCE, GANs & VAEs (and maybe BAC)
NCE, GANs & VAEs (and maybe BAC)
 
K-algebras on quadripartitioned single valued neutrosophic sets
K-algebras on quadripartitioned single valued neutrosophic setsK-algebras on quadripartitioned single valued neutrosophic sets
K-algebras on quadripartitioned single valued neutrosophic sets
 
How many components in a mixture?
How many components in a mixture?How many components in a mixture?
How many components in a mixture?
 
Computational Tools and Techniques for Numerical Macro-Financial Modeling
Computational Tools and Techniques for Numerical Macro-Financial ModelingComputational Tools and Techniques for Numerical Macro-Financial Modeling
Computational Tools and Techniques for Numerical Macro-Financial Modeling
 
Spectral properties of the Goldstino in supersymmetric Bose-Fermi mixtures
Spectral properties of the Goldstino in supersymmetric Bose-Fermi mixturesSpectral properties of the Goldstino in supersymmetric Bose-Fermi mixtures
Spectral properties of the Goldstino in supersymmetric Bose-Fermi mixtures
 
A common random fixed point theorem for rational inequality in hilbert space
A common random fixed point theorem for rational inequality in hilbert spaceA common random fixed point theorem for rational inequality in hilbert space
A common random fixed point theorem for rational inequality in hilbert space
 

More from Ivan Varzinczak

Next Steps in Propositional Horn Contraction
Next Steps in Propositional Horn ContractionNext Steps in Propositional Horn Contraction
Next Steps in Propositional Horn ContractionIvan Varzinczak
 
On the Revision of Action Laws: An Algorithmic Approach
On the Revision of Action Laws: An Algorithmic ApproachOn the Revision of Action Laws: An Algorithmic Approach
On the Revision of Action Laws: An Algorithmic ApproachIvan Varzinczak
 
Action Theory Contraction and Minimal Change
Action Theory Contraction and Minimal ChangeAction Theory Contraction and Minimal Change
Action Theory Contraction and Minimal ChangeIvan Varzinczak
 
Causalidade e dependência em raciocínio sobre ações
Causalidade e dependência em raciocínio sobre açõesCausalidade e dependência em raciocínio sobre ações
Causalidade e dependência em raciocínio sobre açõesIvan Varzinczak
 
Cohesion, Coupling and the Meta-theory of Actions
Cohesion, Coupling and the Meta-theory of ActionsCohesion, Coupling and the Meta-theory of Actions
Cohesion, Coupling and the Meta-theory of ActionsIvan Varzinczak
 
Meta-theory of Actions: Beyond Consistency
Meta-theory of Actions: Beyond ConsistencyMeta-theory of Actions: Beyond Consistency
Meta-theory of Actions: Beyond ConsistencyIvan Varzinczak
 
Domain Descriptions Should be Modular
Domain Descriptions Should be ModularDomain Descriptions Should be Modular
Domain Descriptions Should be ModularIvan Varzinczak
 
Elaborating Domain Descriptions
Elaborating Domain DescriptionsElaborating Domain Descriptions
Elaborating Domain DescriptionsIvan Varzinczak
 
What Is a Good Domain Description? Evaluating and Revising Action Theories in...
What Is a Good Domain Description? Evaluating and Revising Action Theories in...What Is a Good Domain Description? Evaluating and Revising Action Theories in...
What Is a Good Domain Description? Evaluating and Revising Action Theories in...Ivan Varzinczak
 
Regression in Modal Logic
Regression in Modal LogicRegression in Modal Logic
Regression in Modal LogicIvan Varzinczak
 
On the Modularity of Theories
On the Modularity of TheoriesOn the Modularity of Theories
On the Modularity of TheoriesIvan Varzinczak
 
On the Revision of Action Laws: an Algorithmic Approach
On the Revision of Action Laws: an Algorithmic ApproachOn the Revision of Action Laws: an Algorithmic Approach
On the Revision of Action Laws: an Algorithmic ApproachIvan Varzinczak
 
Action Theory Contraction and Minimal Change
Action Theory Contraction and Minimal ChangeAction Theory Contraction and Minimal Change
Action Theory Contraction and Minimal ChangeIvan Varzinczak
 
What Is a Good Domain Description? Evaluating & Revising Action Theories in D...
What Is a Good Domain Description? Evaluating & Revising Action Theories in D...What Is a Good Domain Description? Evaluating & Revising Action Theories in D...
What Is a Good Domain Description? Evaluating & Revising Action Theories in D...Ivan Varzinczak
 

More from Ivan Varzinczak (14)

Next Steps in Propositional Horn Contraction
Next Steps in Propositional Horn ContractionNext Steps in Propositional Horn Contraction
Next Steps in Propositional Horn Contraction
 
On the Revision of Action Laws: An Algorithmic Approach
On the Revision of Action Laws: An Algorithmic ApproachOn the Revision of Action Laws: An Algorithmic Approach
On the Revision of Action Laws: An Algorithmic Approach
 
Action Theory Contraction and Minimal Change
Action Theory Contraction and Minimal ChangeAction Theory Contraction and Minimal Change
Action Theory Contraction and Minimal Change
 
Causalidade e dependência em raciocínio sobre ações
Causalidade e dependência em raciocínio sobre açõesCausalidade e dependência em raciocínio sobre ações
Causalidade e dependência em raciocínio sobre ações
 
Cohesion, Coupling and the Meta-theory of Actions
Cohesion, Coupling and the Meta-theory of ActionsCohesion, Coupling and the Meta-theory of Actions
Cohesion, Coupling and the Meta-theory of Actions
 
Meta-theory of Actions: Beyond Consistency
Meta-theory of Actions: Beyond ConsistencyMeta-theory of Actions: Beyond Consistency
Meta-theory of Actions: Beyond Consistency
 
Domain Descriptions Should be Modular
Domain Descriptions Should be ModularDomain Descriptions Should be Modular
Domain Descriptions Should be Modular
 
Elaborating Domain Descriptions
Elaborating Domain DescriptionsElaborating Domain Descriptions
Elaborating Domain Descriptions
 
What Is a Good Domain Description? Evaluating and Revising Action Theories in...
What Is a Good Domain Description? Evaluating and Revising Action Theories in...What Is a Good Domain Description? Evaluating and Revising Action Theories in...
What Is a Good Domain Description? Evaluating and Revising Action Theories in...
 
Regression in Modal Logic
Regression in Modal LogicRegression in Modal Logic
Regression in Modal Logic
 
On the Modularity of Theories
On the Modularity of TheoriesOn the Modularity of Theories
On the Modularity of Theories
 
On the Revision of Action Laws: an Algorithmic Approach
On the Revision of Action Laws: an Algorithmic ApproachOn the Revision of Action Laws: an Algorithmic Approach
On the Revision of Action Laws: an Algorithmic Approach
 
Action Theory Contraction and Minimal Change
Action Theory Contraction and Minimal ChangeAction Theory Contraction and Minimal Change
Action Theory Contraction and Minimal Change
 
What Is a Good Domain Description? Evaluating & Revising Action Theories in D...
What Is a Good Domain Description? Evaluating & Revising Action Theories in D...What Is a Good Domain Description? Evaluating & Revising Action Theories in D...
What Is a Good Domain Description? Evaluating & Revising Action Theories in D...
 

Recently uploaded

How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfRankYa
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionDilum Bandara
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 

Recently uploaded (20)

How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdf
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An Introduction
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 

First Steps in EL Contraction

  • 1. First Steps in EL Contraction Richard Booth Tommie Meyer Ivan Jos´ Varzinczak e Mahasarakham University Meraka Institute, CSIR Thailand Pretoria, South Africa Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 1 / 24
  • 2. Outline 1 Preliminaries Belief Change Description Logic EL 2 Contraction in EL First Attempt A More Fine-grained Approach Other Types of Contraction 3 Conclusion Summary, Open questions and Further Work Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 2 / 24
  • 3. Outline 1 Preliminaries Belief Change Description Logic EL 2 Contraction in EL First Attempt A More Fine-grained Approach Other Types of Contraction 3 Conclusion Summary, Open questions and Further Work Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 2 / 24
  • 4. Outline 1 Preliminaries Belief Change Description Logic EL 2 Contraction in EL First Attempt A More Fine-grained Approach Other Types of Contraction 3 Conclusion Summary, Open questions and Further Work Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 2 / 24
  • 5. Outline 1 Preliminaries Belief Change Description Logic EL 2 Contraction in EL First Attempt A More Fine-grained Approach Other Types of Contraction 3 Conclusion Summary, Open questions and Further Work Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 3 / 24
  • 6. Revision, Expansion and Contraction Expansion: K + ϕ Revision: K ϕ, with K ϕ |= ϕ and K ϕ |= ⊥ Contraction: K − ϕ |= ϕ Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 4 / 24
  • 7. Revision, Expansion and Contraction Expansion: K + ϕ Revision: K ϕ, with K ϕ |= ϕ and K ϕ |= ⊥ Contraction: K − ϕ |= ϕ Also meaningful for ontologies Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 4 / 24
  • 8. AGM Approach Contraction described on the knowledge level Rationality Postulates (K−1) K − ϕ = Cn(K − ϕ) (K−2) K − ϕ ⊆ K (K−3) If ϕ ∈ K , then K − ϕ = K / (K−4) If |= ϕ, then ϕ ∈ K − ϕ / (K−5) If ϕ ≡ ψ, then K − ϕ = K − ψ (K−6) If ϕ ∈ K , then (K − ϕ) + ϕ = K Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 5 / 24
  • 9. AGM Approach Contraction described on the knowledge level Rationality Postulates (K−1) K − ϕ = Cn(K − ϕ) (K−2) K − ϕ ⊆ K (K−3) If ϕ ∈ K , then K − ϕ = K / (K−4) If |= ϕ, then ϕ ∈ K − ϕ / (K−5) If ϕ ≡ ψ, then K − ϕ = K − ψ (K−6) If ϕ ∈ K , then (K − ϕ) + ϕ = K Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 5 / 24
  • 10. AGM Approach Construction method: Identify the maximally consistent subsets that do not entail ϕ (remainder sets) Pick some non-empty subset of remainder sets Take their intersection: Partial meet Pick all remainder sets: Full meet Pick a single remainder set: Maxichoice Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 6 / 24
  • 11. AGM Approach Example Contraction of {p → r } from Horn theory K = Cn({p → q, q → r }) p∧r →q p∧q →r p→q q→r p→r Maxichoice? Full meet? Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 7 / 24
  • 12. AGM Approach Example Contraction of {p → r } from Horn theory K = Cn({p → q, q → r }) p∧r →q p∧q →r p→q q→r p→r 1 2 Maxichoice? Hmc = Cn({p → q}) or Hmc = Cn({q → r , p ∧ r → q}) Full meet? Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 7 / 24
  • 13. AGM Approach Example Contraction of {p → r } from Horn theory K = Cn({p → q, q → r }) p∧r →q p∧q →r p→q q→r p→r 1 2 Maxichoice? Hmc = Cn({p → q}) or Hmc = Cn({q → r , p ∧ r → q}) Full meet? Hfm = Cn({p ∧ r → q}) Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 7 / 24
  • 14. Outline 1 Preliminaries Belief Change Description Logic EL 2 Contraction in EL First Attempt A More Fine-grained Approach Other Types of Contraction 3 Conclusion Summary, Open questions and Further Work Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 8 / 24
  • 15. Description Logic EL [Baader, 2003] with ⊥ Concepts C ::= A | |⊥|C C | ∃R.C Interpretations I = ∆I , ·I AI ⊆ ∆I , R I ⊆ ∆I × ∆I , I = ∆I , ⊥I = ∅, (C D)I = C I ∩ D I , (∃R.C )I = {a ∈ ∆I | ∃b.(a, b) ∈ R I and b ∈ C I } Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 9 / 24
  • 16. Description Logic EL [Baader, 2003] Axioms C D I |= C D iff C I ⊆ D I TBox T: set of axioms I |= T iff I satisfies every axiom in T T |= C D iff for all I if I |= T then I |= C D Cn(T) = {C D | T |= C D} Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 10 / 24
  • 17. Description Logic EL [Baader, 2003] Example T = Cn({A B, B ∃R.A}) A ∃R.A B A B ∃R.A A B B ∃R.A A ∃R.A Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 11 / 24
  • 18. Outline 1 Preliminaries Belief Change Description Logic EL 2 Contraction in EL First Attempt A More Fine-grained Approach Other Types of Contraction 3 Conclusion Summary, Open questions and Further Work Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 12 / 24
  • 19. Motivation Let T be a TBox and Φ be a set of axioms Contract T with Φ we want T |= Φ Some axiom in Φ should not follow from T anymore Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 13 / 24
  • 20. Following Delgrande’s Approach [KR’2008] Definition (Remainder Sets) For a belief set T, X ∈ T ↓ Φ iff X ⊆T X |= Φ for every X s.t. X ⊂ X ⊆ T, X |= Φ. We call T ↓ Φ the remainder sets of T w.r.t. Φ Do they exist? EL is compact and has a Tarskian consequence relation Definition (Selection Functions) A selection function σ is a function from P(P(LEL )) to P(P(LEL )) s.t. σ(T ↓ Φ) = {T} if T ↓ Φ = ∅, and σ(T ↓ Φ) ⊆ T ↓ Φ otherwise. Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 14 / 24
  • 21. Following Delgrande’s Approach [KR’2008] Definition (Remainder Sets) For a belief set T, X ∈ T ↓ Φ iff X ⊆T X |= Φ for every X s.t. X ⊂ X ⊆ T, X |= Φ. We call T ↓ Φ the remainder sets of T w.r.t. Φ Do they exist? EL is compact and has a Tarskian consequence relation Definition (Selection Functions) A selection function σ is a function from P(P(LEL )) to P(P(LEL )) s.t. σ(T ↓ Φ) = {T} if T ↓ Φ = ∅, and σ(T ↓ Φ) ⊆ T ↓ Φ otherwise. Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 14 / 24
  • 22. Following Delgrande’s Approach [KR’2008] Definition (Remainder Sets) For a belief set T, X ∈ T ↓ Φ iff X ⊆T X |= Φ for every X s.t. X ⊂ X ⊆ T, X |= Φ. We call T ↓ Φ the remainder sets of T w.r.t. Φ Do they exist? EL is compact and has a Tarskian consequence relation Definition (Selection Functions) A selection function σ is a function from P(P(LEL )) to P(P(LEL )) s.t. σ(T ↓ Φ) = {T} if T ↓ Φ = ∅, and σ(T ↓ Φ) ⊆ T ↓ Φ otherwise. Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 14 / 24
  • 23. Following Delgrande’s Approach Definition (Partial Meet Contraction) Given a selection function σ, −σ is a partial meet contraction iff T −σ Φ = σ(T ↓ Φ). Definition (Maxichoice and Full Meet) Given a selection function σ, −σ is a maxichoice contraction iff σ(T ↓ Φ) is a singleton set. It is a full meet contraction iff σ(T ↓ Φ) = T ↓ Φ when T ↓ Φ = ∅. Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 15 / 24
  • 24. Following Delgrande’s Approach Definition (Partial Meet Contraction) Given a selection function σ, −σ is a partial meet contraction iff T −σ Φ = σ(T ↓ Φ). Definition (Maxichoice and Full Meet) Given a selection function σ, −σ is a maxichoice contraction iff σ(T ↓ Φ) is a singleton set. It is a full meet contraction iff σ(T ↓ Φ) = T ↓ Φ when T ↓ Φ = ∅. Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 15 / 24
  • 25. Following Delgrande’s Approach Example Contraction of {A ∃R.A} from T = Cn({A B, B ∃R.A}) A ∃R.A B A B ∃R.A A B B ∃R.A A ∃R.A Maxichoice? Full meet? Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 16 / 24
  • 26. Following Delgrande’s Approach Example Contraction of {A ∃R.A} from T = Cn({A B, B ∃R.A}) A ∃R.A B A B ∃R.A A B B ∃R.A A ∃R.A 1 Maxichoice? Tmc = Cn({A 2 B}) or Tmc = Cn({B ∃R.A, A ∃R.A B}) Full meet? Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 16 / 24
  • 27. Following Delgrande’s Approach Example Contraction of {A ∃R.A} from T = Cn({A B, B ∃R.A}) A ∃R.A B A B ∃R.A A B B ∃R.A A ∃R.A 1 Maxichoice? Tmc = Cn({A 2 B}) or Tmc = Cn({B ∃R.A, A ∃R.A B}) Full meet? Tfm = Cn({A ∃R.A B}) Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 16 / 24
  • 28. Following Delgrande’s Approach Example Contraction of {A ∃R.A} from T = Cn({A B, B ∃R.A}) A ∃R.A B A B ∃R.A A B B ∃R.A A ∃R.A 1 Maxichoice? Tmc = Cn({A 2 B}) or Tmc = Cn({B ∃R.A, A ∃R.A B}) Full meet? Tfm = Cn({A ∃R.A B}) What about T = Cn({A ∃R.A B, A B ∃R.A})? Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 16 / 24
  • 29. Following Delgrande’s Approach Example Contraction of {A ∃R.A} from T = Cn({A B, B ∃R.A}) A ∃R.A B A B ∃R.A A B B ∃R.A A ∃R.A 1 Maxichoice? Tmc = Cn({A 2 B}) or Tmc = Cn({B ∃R.A, A ∃R.A B}) Full meet? Tfm = Cn({A ∃R.A B}) What about T = Cn({A ∃R.A B, A B ∃R.A})? 2 Tfm ⊆ T ⊆ Tmc , but there is no partial meet contraction yielding T ! Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 16 / 24
  • 30. Outline 1 Preliminaries Belief Change Description Logic EL 2 Contraction in EL First Attempt A More Fine-grained Approach Other Types of Contraction 3 Conclusion Summary, Open questions and Further Work Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 17 / 24
  • 31. Beyond Partial Meet [Booth et al., IJCAI’09] Definition (Infra-Remainder Sets) For belief sets T and X , X ∈ T ⇓ Φ iff there is some X ∈ T ↓ Φ s.t. ( T ↓ Φ) ⊆ X ⊆ X . We call T ⇓ Φ the infra-remainder sets of T w.r.t. Φ. Infra-remainder sets contain all belief sets between some remainder set and the intersection of all remainder sets Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 18 / 24
  • 32. Beyond Partial Meet [Booth et al., IJCAI’09] Definition (EL Contraction) An infra-selection function τ is a function from P(P(LEL )) to P(LEL ) s.t. τ (T ⇓ Φ) = T whenever |= Φ, and τ (T ⇓ Φ) ∈ T ⇓ Φ otherwise. A contraction function −τ is an EL-contraction iff T −τ Φ = τ (T ⇓ Φ). Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 19 / 24
  • 33. A Representation Result Basic postulates for EL contraction (T − 1) T − Φ = Cn(T − Φ) (T − 2) T − Φ ⊆ T (T − 3) If Φ ⊆ T then T − Φ = T (T − 4) If |= Φ then Φ ⊆ T − Φ (T − 5) If Cn(Φ) = Cn(Ψ) then T − Φ = T − Ψ (T − 6) If ϕ ∈ T (T − Φ) then there is a T such that (T ↓ Φ) ⊆ T ⊆ T, T |= Φ, and T + {ϕ} |= Φ Conjecture Every EL contraction satisfies (T − 1)–(T − 6). Conversely, every contraction function satisfying (T − 1)–(T − 6) is an EL contraction. Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 20 / 24
  • 34. A Representation Result Basic postulates for EL contraction (T − 1) T − Φ = Cn(T − Φ) (T − 2) T − Φ ⊆ T (T − 3) If Φ ⊆ T then T − Φ = T (T − 4) If |= Φ then Φ ⊆ T − Φ (T − 5) If Cn(Φ) = Cn(Ψ) then T − Φ = T − Ψ (T − 6) If ϕ ∈ T (T − Φ) then there is a T such that (T ↓ Φ) ⊆ T ⊆ T, T |= Φ, and T + {ϕ} |= Φ Conjecture Every EL contraction satisfies (T − 1)–(T − 6). Conversely, every contraction function satisfying (T − 1)–(T − 6) is an EL contraction. Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 20 / 24
  • 35. Outline 1 Preliminaries Belief Change Description Logic EL 2 Contraction in EL First Attempt A More Fine-grained Approach Other Types of Contraction 3 Conclusion Summary, Open questions and Further Work Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 21 / 24
  • 36. Other Types of Contraction Inconsistency-based Contraction [Delgrande, KR’2008] Let T be a TBox and Φ be a set of axioms Contract T ‘making room’ for Φ We want T + Φ |= ⊥ Package Contraction [Booth et al., IJCAI’09] Let T be a TBox and Φ be a set of axioms Contract T so that none of the axioms in Φ follows from it Removal of all sentences in Φ from T Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 22 / 24
  • 37. Other Types of Contraction Inconsistency-based Contraction [Delgrande, KR’2008] Let T be a TBox and Φ be a set of axioms Contract T ‘making room’ for Φ We want T + Φ |= ⊥ Package Contraction [Booth et al., IJCAI’09] Let T be a TBox and Φ be a set of axioms Contract T so that none of the axioms in Φ follows from it Removal of all sentences in Φ from T Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 22 / 24
  • 38. Outline 1 Preliminaries Belief Change Description Logic EL 2 Contraction in EL First Attempt A More Fine-grained Approach Other Types of Contraction 3 Conclusion Summary, Open questions and Further Work Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 23 / 24
  • 39. Summary, Open questions and Further Work Summary Basic AGM account of contraction for EL Weaker than partial meet contraction Open questions Are infra-remainder sets enough? Is Cn(.) what we really want? Kernel contraction? (Renata knows the answer ) What about the syntax? (A, A ∃R.A, A ∃R.∃R.A,. . . ) Current and Future Work Answer questions above Full AGM setting: extended postulates Relation to justifications in ontology repair Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 24 / 24
  • 40. Summary, Open questions and Further Work Summary Basic AGM account of contraction for EL Weaker than partial meet contraction Open questions Are infra-remainder sets enough? Is Cn(.) what we really want? Kernel contraction? (Renata knows the answer ) What about the syntax? (A, A ∃R.A, A ∃R.∃R.A,. . . ) Current and Future Work Answer questions above Full AGM setting: extended postulates Relation to justifications in ontology repair Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 24 / 24
  • 41. Summary, Open questions and Further Work Summary Basic AGM account of contraction for EL Weaker than partial meet contraction Open questions Are infra-remainder sets enough? Is Cn(.) what we really want? Kernel contraction? (Renata knows the answer ) What about the syntax? (A, A ∃R.A, A ∃R.∃R.A,. . . ) Current and Future Work Answer questions above Full AGM setting: extended postulates Relation to justifications in ontology repair Booth, Meyer, Varzinczak (MU/KSG) EL Contraction 24 / 24