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A Distributed Tableau Algorithm for Package-based Description Logics
1. A Distributed Tableau Algorithm for Package-based Description Logics Jie Bao 1 , Doina Caragea 2 and Vasant G Honavar 1 1 Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, IA 50011-1040, USA. {baojie, honavar}@cs.iastate.edu 2 Department of Computing and Information Sciences Kansas State University, Manhattan, KS 66506, USA dcaragea@ksu.edu 2nd International Workshop on Context Representation and Reasoning (CRR 2006) @ ECAI 2006, Aug 29, 2006, Riva del Garda, Italy
10. Package: Example O 1 (General Animal) O 2 (Pet) It uses ALCP, but not ALCP C
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12. Partially Overlapping Models x x’ Δ I 1 Δ I 2 C I 1 C I 2 Δ I 3 r 13 r 23 x’’ C I 3 x C I Global interpretation obtained from local Interpretations by merging shared individuals r 12
13. Model Projection x C I x C I 1 x’ C I 2 x’’ C I 3 Global model local models
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16. Tableau Algorithm: Example Dog(goofy) Animal(goofy) ( eats.DogFood)(goofy) eats(goofy,foo) DogFood(foo) goofy L(goofy)={Dog, Animal, eats.DogFood } foo L(foo)={DogFood } eats ABox Representation Completion Tree Representation Note: both representations are simplified for demostration purpose
17. Federated Reasoning Chef: Hello there, children! Where does Kyle move to? Chef: We are in South Park, Colorado; San Francisco is in California; Colorado is far from California. Stan: So they are far from us. Too Bad. Stan: Hey, Chef . Is Kyle’s new home far from us? Cartman: San Francisco, I guess.
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19. Tableau Projection x 1 {A 1 } {A 2 } {A 3 } x 2 x 4 x 1 {B 1 } {B 3 } {B 2 } x 3 x 4 The (conceptual) global tableau Local Reasoner for package A Local Reasoner for package B Shared individuals mean partially overlapped local models x 1 {A 1 ,B 1 } {A 2 } {A 3 ,B 3 } {B 2 } x 2 x 3 x 4
20. Model Projection x C I x C I 1 x’ C I 2 x’’ C I 3 Global model local models
25. ALCP C Expansion Example (3) L 2 (x)={ P, P⊔B, P⊔ F,B, F} x x L 1 (x)={ B, F , B⊔F, F } T 2 T 1 r(x,B) r(x, F) (x) L 1 (x)={A, A⊔C,C} y z L 2 (y)={A, A⊔ R.B, B⊔(A⊓ C), R.B, B} P T 1 T 2 L 2 (z)={B, A⊔ R.B, B⊔(A⊓ C), R.B, A⊓ C, A, C} y L 1 (z)={A, C , A⊔C, C } z r(z,A) r(z, C) (x) r(z,A) (x) Detect Inter-module Unsatisfiability 2:P is unsatisfiable Reasoning from Local Point of View 1:A is unsatisfiable witnessed by P 2 is satisfiable witnessed by P 1 P 1 : f 1 : B v 1 : F g , P 2 : f 1 : P v 1 : B ; 2 : P v : 1 : F g P 1 : f 1 : A v 1 : C g P 2 : f 1 : A v 9 2 : R : ( 2 : B ) ; 2 : B v 1 : A u ( : 1 : C ) g
26. Soundness β α α α α β α or or α A A A B A’ A’’ A’ A B’ infer (a) Augmenting (c) Reporting (b) Searching A is consistent iff A’ is consistent A is consistent iff A’ is consistent or A’’ is consistent (A,B) is consistent iff (A,B’) is consistent send
30. Other Tableau Projections Distributed Description Logics (DDL) [ Serafini and Tamilin 2004, 2005] x 1 x 2 x 3 x 4 x 1 x 2 x 3 x 4 x 3 x 5 x 5 f B 1 u : B 2 ; ¢ ¢ ¢ g f B 1 u : B 2 ; ¢ ¢ ¢ g
31. Other Tableau Projections (2) x 1 x 2 x 3 x 4 x 1 x 2 x 4 x 5 x 3 x 6 E-Connections [ Grau 2005] x 5 x 6 E E {A 1 } {A 1 } {A 2 } {A 3 } {B 1 } {B 2 } {B 3 } {A 2 } {A 3 } {B 1 } {B 2 } {B 3 }
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Editor's Notes
Merge soundness and completeness, termination slides