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Local Closed World Semantics for Description Logics
                                           Adila Krisnadhi, Kunal Sengupta and Pascal Hitzler
                      Knowledge Engineering Lab – Kno.e.sis Center – Wright State University – Dayton, Ohio, USA


  1. Introduction                                                        2. Grounded Circumscription

 Semantics of OWL (and DLs in general) adheres to the Open               Circumscription employs minimization straightforwardly.
 World Assumption (OWA): statements that are not logical                 Circumscription in DLs: augment a KB with a circumscription pattern consisting
 consequences of a knowledge base (KB) is not considered false.          disjoint sets minimized, fixed, varying predicates and a preference relation on
 However, the Closed World Assumption (CWA) — statements that            interpretations to pick minimal models as the preferred models.
 are not logical consequences of KB are considered false — is            Drawbacks of circumscriptive DLs:
 preferable in certain situations, e.g., when data is retrieved from a     undecidable when TBox is non-empty and some role is minimized;
 database, or data is considered complete w.r.t. application.              extensions of minimized predicates may contain non-named individuals;
 Need languages that adhere to the Local Closed World Assumption         Grounded circumscription (GC) simplifies circumscription:
 (LCWA): both OWA & CWA as modeling features in one language.              only consider minimized and non-minimized predicates;
 Users want to specify that individuals in the extension of a              extensions of minimized predicates are restricted to named individuals, hence is
 predicate (concept/role name) are those which are necessarily             decidable if the underlying DL is decidable;
 required, i.e., predicates are minimized.                                 roles can be minimized without losing decidability even when the TBox is not empty.


  3. Key Definitions                                                      4. Example (1)
                                                                                hasAuthor(paper1, author1)            hasAuthor(paper1, author2)
 For a DL L, GC-L-KB is a pair (K, M ) where K is an L-KB,
                                                                                hasAuthor(paper2, author3)                 ∀hasAuthor.Author.
 M ⊆ NC ∪ NR. Let Ind(K) be set of named inviduals in K.
 Concept/role name W is minimized w.r.t. K if W ∈ M                      Take (i) (≤2 hasAuthor.Author)(paper1); (ii) ¬hasAuthor(paper, author3)
 For any models I and J of K, I is smaller than J w.r.t. M iff            Classical semantics: neither (i) nor (ii) is a logical consequence
   ∆I = ∆J and aI = aJ for every aI ∈ ∆J ;                               Minimize hasAuthor without UNA: (i) is a GC-inference
   W I ⊆ W J for every W ∈ M ; and                                       Minimize hasAuthor with UNA: (i) and (ii) are GC-inferences
   there exists a W ∈ M such that W I ⊂ W J
 A model I of K is a grounded model w.r.t. M iff
   for A ∈ M ∩ NC , AI ⊆ {bI | b ∈ Ind(K)}                               5. Example (2)
   for r ∈ M ∩ NR, r I ⊆ {(bI , cI ) | b, c ∈ Ind(K)}
 I is a GC-model of a GC-L-KB (K, M ) iff:                                          Bear(polarBear)
   I is a grounded model of K w.r.t. M ;                                           ∃isHabitatFor.(Bear        EndangeredSpecies)(arcticSea)
   I is minimal w.r.t. M .                                               Consider EndangeredSpecies(polarBear)
 A statement/assertion is a GC-logical consequence (GC-inference)          In the original circumscriptive DLs: is not a logical consequence
 of (K, M ) if every GC-model of (K, M ) satisfies it.                      Under GC-semantics: is a (GC)-logical consequence


  6. Decidability of GC-KB Satisfiability

For any DL L with nominals, concept disjunction & concept products: if KB-satisfiability is decidable for L , then so is GC-KB satisfiability.

Proof (sketch):
 Let (K, M ) be a GC-L-KB such that M = MA ∪ MR where MA = {A1, . . . , An} and MR = {r1, . . . , rm} with n, m ≥ 0 and Ai, rj are
 minimized concept and role names w.r.t. K.
 The set G(K,M ) that contains precisely all tuples (X1, . . . , Xn, Y1, . . . , Ym) where all Xi ⊆ Ind(K) and Yj ⊆ Ind(K) × Ind(K) is finite.
 Given a tuple G = (X1, . . . , Xn, Y1, . . . , Ym} ∈ GK,M , let KG be K plus the following axioms:
              Ai ≡      {a} for every a ∈ Xi and 1 ≤ i ≤ n                  rj ≡      ({a} × {b}) for every pair (a, b) ∈ Yj and 1 ≤ j ≤ m
 If (K, M ) has a grounded model I then there exists G ∈ G(K,M ) such that KG has a model J which coincides with I on all minimized predicates.
 If there exists G ∈ G(K,M ) such that KG has a model J , then (K, M ) has a grounded model I which coincides with J on all minimized predicates.
 The set G(K,M ) = {G ∈ G(K,M ) | KG has a (classical) model} is finite and membership in G(K,M ) is decidable if L is decidable.
 There is a pointwise (partial) ordering of tuples in G(K,M ) induced by ⊆ on the tuple components.
 (K, M ) has a GC-model if and only if G(K,M ) is non-empty where G(K,M ) = {G ∈ G(K,M ) | G is minimal in (G(K,M ), )}
 The theorem follows since the emptiness of G(K,M ) is decidable.


  7. Future/Ongoing Works                                                                                                   References

                                                                                                                           Bonatti, P.A., Lutz, C., Wolter, F.: The
 Tableau algorithm deciding GC-KB-satisfiability, e.g., for ALC:                                                            Complexity of Circumscription in
   Add two grounding rules (for minimized concepts and minimized roles) to the standard ALC tableau algorithm.             Description Logic. Journal of Artificial
 Inference problems beyond GC-KB-satisfiability:                                                                            Intelligence Research 35, 717–773 (2009)

   employs the approach of tableau algorithm for circumscriptive DLs                                                       Grimm, S., Hitzler, P.: A Preferential
   additional check for preference clash                                                                                   Tableaux Calculus for Circumscriptive
 Other future work: investigate exact worst-case complexity of GC-reasoning.                                               ALCO. In Proc. of RR’09. LNCS, vol.
                                                                                                                           5837, pp. 40–54.


                             Knowledge Engineering Lab. - Kno.e.sis Center - Wright State University - Dayton, Ohio, USA
                   {adila,kunal,pascal}@knoesis.org              http://knoesis.wright.edu/faculty/pascal/knoelab.html

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Local Closed World Semantics - DL 2011 Poster

  • 1. Local Closed World Semantics for Description Logics Adila Krisnadhi, Kunal Sengupta and Pascal Hitzler Knowledge Engineering Lab – Kno.e.sis Center – Wright State University – Dayton, Ohio, USA 1. Introduction 2. Grounded Circumscription Semantics of OWL (and DLs in general) adheres to the Open Circumscription employs minimization straightforwardly. World Assumption (OWA): statements that are not logical Circumscription in DLs: augment a KB with a circumscription pattern consisting consequences of a knowledge base (KB) is not considered false. disjoint sets minimized, fixed, varying predicates and a preference relation on However, the Closed World Assumption (CWA) — statements that interpretations to pick minimal models as the preferred models. are not logical consequences of KB are considered false — is Drawbacks of circumscriptive DLs: preferable in certain situations, e.g., when data is retrieved from a undecidable when TBox is non-empty and some role is minimized; database, or data is considered complete w.r.t. application. extensions of minimized predicates may contain non-named individuals; Need languages that adhere to the Local Closed World Assumption Grounded circumscription (GC) simplifies circumscription: (LCWA): both OWA & CWA as modeling features in one language. only consider minimized and non-minimized predicates; Users want to specify that individuals in the extension of a extensions of minimized predicates are restricted to named individuals, hence is predicate (concept/role name) are those which are necessarily decidable if the underlying DL is decidable; required, i.e., predicates are minimized. roles can be minimized without losing decidability even when the TBox is not empty. 3. Key Definitions 4. Example (1) hasAuthor(paper1, author1) hasAuthor(paper1, author2) For a DL L, GC-L-KB is a pair (K, M ) where K is an L-KB, hasAuthor(paper2, author3) ∀hasAuthor.Author. M ⊆ NC ∪ NR. Let Ind(K) be set of named inviduals in K. Concept/role name W is minimized w.r.t. K if W ∈ M Take (i) (≤2 hasAuthor.Author)(paper1); (ii) ¬hasAuthor(paper, author3) For any models I and J of K, I is smaller than J w.r.t. M iff Classical semantics: neither (i) nor (ii) is a logical consequence ∆I = ∆J and aI = aJ for every aI ∈ ∆J ; Minimize hasAuthor without UNA: (i) is a GC-inference W I ⊆ W J for every W ∈ M ; and Minimize hasAuthor with UNA: (i) and (ii) are GC-inferences there exists a W ∈ M such that W I ⊂ W J A model I of K is a grounded model w.r.t. M iff for A ∈ M ∩ NC , AI ⊆ {bI | b ∈ Ind(K)} 5. Example (2) for r ∈ M ∩ NR, r I ⊆ {(bI , cI ) | b, c ∈ Ind(K)} I is a GC-model of a GC-L-KB (K, M ) iff: Bear(polarBear) I is a grounded model of K w.r.t. M ; ∃isHabitatFor.(Bear EndangeredSpecies)(arcticSea) I is minimal w.r.t. M . Consider EndangeredSpecies(polarBear) A statement/assertion is a GC-logical consequence (GC-inference) In the original circumscriptive DLs: is not a logical consequence of (K, M ) if every GC-model of (K, M ) satisfies it. Under GC-semantics: is a (GC)-logical consequence 6. Decidability of GC-KB Satisfiability For any DL L with nominals, concept disjunction & concept products: if KB-satisfiability is decidable for L , then so is GC-KB satisfiability. Proof (sketch): Let (K, M ) be a GC-L-KB such that M = MA ∪ MR where MA = {A1, . . . , An} and MR = {r1, . . . , rm} with n, m ≥ 0 and Ai, rj are minimized concept and role names w.r.t. K. The set G(K,M ) that contains precisely all tuples (X1, . . . , Xn, Y1, . . . , Ym) where all Xi ⊆ Ind(K) and Yj ⊆ Ind(K) × Ind(K) is finite. Given a tuple G = (X1, . . . , Xn, Y1, . . . , Ym} ∈ GK,M , let KG be K plus the following axioms: Ai ≡ {a} for every a ∈ Xi and 1 ≤ i ≤ n rj ≡ ({a} × {b}) for every pair (a, b) ∈ Yj and 1 ≤ j ≤ m If (K, M ) has a grounded model I then there exists G ∈ G(K,M ) such that KG has a model J which coincides with I on all minimized predicates. If there exists G ∈ G(K,M ) such that KG has a model J , then (K, M ) has a grounded model I which coincides with J on all minimized predicates. The set G(K,M ) = {G ∈ G(K,M ) | KG has a (classical) model} is finite and membership in G(K,M ) is decidable if L is decidable. There is a pointwise (partial) ordering of tuples in G(K,M ) induced by ⊆ on the tuple components. (K, M ) has a GC-model if and only if G(K,M ) is non-empty where G(K,M ) = {G ∈ G(K,M ) | G is minimal in (G(K,M ), )} The theorem follows since the emptiness of G(K,M ) is decidable. 7. Future/Ongoing Works References Bonatti, P.A., Lutz, C., Wolter, F.: The Tableau algorithm deciding GC-KB-satisfiability, e.g., for ALC: Complexity of Circumscription in Add two grounding rules (for minimized concepts and minimized roles) to the standard ALC tableau algorithm. Description Logic. Journal of Artificial Inference problems beyond GC-KB-satisfiability: Intelligence Research 35, 717–773 (2009) employs the approach of tableau algorithm for circumscriptive DLs Grimm, S., Hitzler, P.: A Preferential additional check for preference clash Tableaux Calculus for Circumscriptive Other future work: investigate exact worst-case complexity of GC-reasoning. ALCO. In Proc. of RR’09. LNCS, vol. 5837, pp. 40–54. Knowledge Engineering Lab. - Kno.e.sis Center - Wright State University - Dayton, Ohio, USA {adila,kunal,pascal}@knoesis.org http://knoesis.wright.edu/faculty/pascal/knoelab.html