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Representing and Reasoning with Modular Ontologies Jie Bao   and Vasant G Honavar 1 Artificial Intelligence Research Laboratory,  Department of Computer Science,  Iowa State University,  Ames,  IA 50011-1040, USA.  {baojie, honavar}@cs.iastate.edu AAAI 2006 Fall Symposium on Semantic Web for Collaborative Knowledge Acquisition (SweCka 2006),  October 13-15 2006, Hyatt Crystal City, Arlington, VA, USA
Outline ,[object Object],[object Object],[object Object],[object Object]
Modularity
The Need for Modular Ontologies(MO) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Modular Ontology Example Computer Science Dept Ontology Registrar’s Office Ontology G r a d u a t e O K v : 9 f a i l s : C o r e C o u r s e G r a d u a t e O K v P r e l i m O K P r e l i m O K ( J i e ) C s C o r e C o u r s e ( c s 5 1 1 ) f a i l s ( 3 3 0 4 , c s 5 1 1 ) S S N ( 3 3 0 4 , 1 2 3 4 5 6 7 8 9 ) Semantic Relations C s C o r e C o u r s e v C o r e C o u r s e J i e = 3 3 0 4 Hidden Knowledge
Outline ,[object Object],[object Object],[object Object],[object Object]
Package-based Description Logics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],General Pet Wild Livestock Animal ontology PetDog Pet Dog General
Package: Example O 1  (General Animal) O 2  (Pet) It uses ALCP, but not ALCP C
P-DL Semantics ,[object Object],[object Object],[object Object],Syntax Semantics Man  Human In every world ( interpretation ), anybody who is a Man is also a Human { ï€ą x|Man(x)}    { ï€ą x|Human(x)}
Interpretations Interpretation :  In every world that conforms to the ontology   Ontology: Dog   I Animal I ,[object Object],goofy I ,[object Object],eats I ,[object Object],DogFood I
Tableau 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
Local Interpretations Animal I Carnivore I Dog I goofy foo I Dog I Pet I PetDog I pluto eats I 1 1 1 2 2 2 2 2 DogFood I 2 Animal I 2 O 1 O 2 A modular ontology may have multiple (local) interpretations for its modules
Semantics of Importing O 1 O 2 importing Animal I Carnivore I Dog I foo I Dog I Pet I PetDog I pluto eats I 1 1 1 2 2 2 2 2 DogFood I 2 Animal I 2 goofy    pluto, Dog I 1 Dog I 2 = goofy
Model Projection x C I x C I 1 x’ C I 2 x’’ C I 3 Global model local models
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
Build  Tableau for ALCP C Tableau Expansion for ALCP C  with acyclic importing
Messages y y {C?} T 1 T 2 y y {C} C(y) T 1 T 2
Advantages ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object]
Selective Knowledge Hiding Locally visible : Has date Globally visible : Has activity Bob’ schedule ontology Alice’ schedule ontology
Scope Limitation Modifier   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SLM: example A schedule ontology Hidden: details of the activity Visible: there is an activity K v K h
Concealable Reasoning ,[object Object],[object Object],Queries Yes Unknown
Why It Is Possible ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example: Graph Reachability unknown YES a b c d OWA: there may be another path that connects a and d but is not included in the visible graph (thus a->d does not imply b ->c   )
A Concealable Reasoner Unknown (Hidden knowledge) Y N Y N Unknown (Incomplete knowledge) Yes Subsumption query
Safe Scope Policy ,[object Object],[object Object],[object Object]
History-safe Scope Policy ,[object Object],[object Object],[object Object],Open problem: history-safe scope policy for expressive P-DL a b c d e YES YES a b c d e
Outline ,[object Object],[object Object],[object Object],[object Object]
Related Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
More Details ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]

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Representing and Reasoning with Modular Ontologies

  • 1. Representing and Reasoning with Modular Ontologies Jie Bao and Vasant G Honavar 1 Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, IA 50011-1040, USA. {baojie, honavar}@cs.iastate.edu AAAI 2006 Fall Symposium on Semantic Web for Collaborative Knowledge Acquisition (SweCka 2006), October 13-15 2006, Hyatt Crystal City, Arlington, VA, USA
  • 2.
  • 4.
  • 5. Modular Ontology Example Computer Science Dept Ontology Registrar’s Office Ontology G r a d u a t e O K v : 9 f a i l s : C o r e C o u r s e G r a d u a t e O K v P r e l i m O K P r e l i m O K ( J i e ) C s C o r e C o u r s e ( c s 5 1 1 ) f a i l s ( 3 3 0 4 , c s 5 1 1 ) S S N ( 3 3 0 4 , 1 2 3 4 5 6 7 8 9 ) Semantic Relations C s C o r e C o u r s e v C o r e C o u r s e J i e = 3 3 0 4 Hidden Knowledge
  • 6.
  • 7.
  • 8. Package: Example O 1 (General Animal) O 2 (Pet) It uses ALCP, but not ALCP C
  • 9.
  • 10.
  • 11. Tableau 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
  • 12. Local Interpretations Animal I Carnivore I Dog I goofy foo I Dog I Pet I PetDog I pluto eats I 1 1 1 2 2 2 2 2 DogFood I 2 Animal I 2 O 1 O 2 A modular ontology may have multiple (local) interpretations for its modules
  • 13. Semantics of Importing O 1 O 2 importing Animal I Carnivore I Dog I foo I Dog I Pet I PetDog I pluto eats I 1 1 1 2 2 2 2 2 DogFood I 2 Animal I 2 goofy  pluto, Dog I 1 Dog I 2 = goofy
  • 14. Model Projection x C I x C I 1 x’ C I 2 x’’ C I 3 Global model local models
  • 15. 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
  • 16. Build Tableau for ALCP C Tableau Expansion for ALCP C with acyclic importing
  • 17. Messages y y {C?} T 1 T 2 y y {C} C(y) T 1 T 2
  • 18.
  • 19.
  • 20. Selective Knowledge Hiding Locally visible : Has date Globally visible : Has activity Bob’ schedule ontology Alice’ schedule ontology
  • 21.
  • 22. SLM: example A schedule ontology Hidden: details of the activity Visible: there is an activity K v K h
  • 23.
  • 24.
  • 25. Example: Graph Reachability unknown YES a b c d OWA: there may be another path that connects a and d but is not included in the visible graph (thus a->d does not imply b ->c )
  • 26. A Concealable Reasoner Unknown (Hidden knowledge) Y N Y N Unknown (Incomplete knowledge) Yes Subsumption query
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.