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Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
Representing and Reasoning with Modular Ontologies (2007)
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Representing and Reasoning with Modular Ontologies (2007)

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  • 1. Representing and Reasoning with Modular Ontologies Ph.D. Dissertation Defense Major advisor: Vasant Honavar Jie Bao Artificial Intelligence Research Laboratory Computer Science Department Iowa State University Ames, IA USA 50011 Email: baojie@cs.iastate.edu July 10, 2007
  • 2. Outline
    • Introduction
      • Motivation, desiderata and state-of-the-art of modular ontologies
    • Representing Modular Ontology
      • Using Package-based Description Logics (P-DL)
    • Reasoning with Modular Ontology
      • Distributed reasoning in P-DL using tableau algorithm
    • Privacy-Preserving Reasoning with Hidden Knowledge
    • Collaborative Building of Modular Ontologies
  • 3. From Web to Semantic Web Ontology: a “PhD Candidate” is a “Student”
  • 4. Semantic Web Figure courtesy of Tim Berners-Lee, AAAI 2006
  • 5. A Very Very Short DL Primer
    • Description Logics (DL):
      • a knowledge representation formalism to describe ontologies
      • the foundation for web ontology languages, e.g., OWL
    • Ontology example
      • A Dog is an Animal
      • A Dog eats some DogFood
      • goofy is a Dog
    concept role individual axioms
  • 6. DL Families
    • ALC
      • ⊔ (disjunction) : Child = Boy ⊔ Girl
      • ⊓ (conjunction) : Mother = Female ⊓ Parent
      •  (existential restriction) : Parent =  hasChild.Human
      •  (value restriction) : Human ⊑  hasBrother.Man
      •  (negation) : Boy ⊑  Girl
    • SHOIQ
      • S =ALC+ transitive role : Trans(hasSibling)
      • H (role hierarchy) : hasBrother ⊑ hasSibling
      • O (nominal , i.e., concept that has single instance): Sun, France
      • I (inverse role) : hasChild = hasParent -
      • Q (qualified number restriction) : Human ⊑ (=2 hasParent.Human)
  • 7. From Web Pages to Ontologies
    • Web: Network effect
    [Diagram: Joanne Luciano, Predictive Medicine ; Drug discovery demo using RDF, Sideran Seamark and Oracle 10g]
    • Web pages: web  Ontologies : semantic web
  • 8. Distributed, Modular Ontologies
    • Distributed ontology modules
    • Are produced by autonomous participants
      • Are limited in their scope
      • Represent different points of view
      • Have (potentially) partially overlapping domains
    • Lack global semantics
      • Need contextualized semantics
    • Need selective or partial knowledge reuse
    • Need distributed inference algorithms without forcing ontology integration
    • Should facilitate network effect
  • 9. Analogy: Paper Writing Citation is not copy+paste, hence does not result in a single, combined document Recent development in modular ontologies… In this paper, we present two algorithms A and B to … (Alice, 2001) (Bob, 2007) Combining Ontologies Ontology Modularization Recent development in modular ontologies… In this paper, we extend the algorithm A proposed by (Alice,2001) … Same global domain: modular ontologies Multiple independent participants Possible (partial) reuse Contextualized Semantics
  • 10. Modular Ontology Languages: State-of-the-art overview C Є (SHOIN(D)) OWL 1998 2002 2003 2004 2005 2006 2007 C-OWL CTXML E-Connections P-DL DDL DFOL DDL with Role  Concept Mapping C Є (SHIF(D)) IHN + s DL ALCP C SHOIQP
  • 11. Ontology Reuse in OWL: Syntactic Importing
    • The OWL primitive intended to support ontology reuse is owl:import
    • One can use owl:import to copy-and-paste an ontology into another
  • 12. Analogy: Paper Writing in OWL fashion copy+paste
    • no partial reuse
    • loss of context
    Recent development in modular ontologies… In this paper, we present two algorithms A and B to … (Alice, 2001) (Bob, 2007) Combining Ontologies Ontology Modularization Recent development in modular ontologies… In this paper, we extend the algorithm A proposed by (Alice,2001) …
  • 13. DDL
    • Distributed Description Logics (DDL) [Borgida & Serafini, 2002]
      • Allows “bridge rules” between concepts across ontology modules
      • Bridge rules between roles are similar
    • Semantics given by “domain relations”
    (onto) (into) Pet Animal Dog I 1 Dog Pet I 2 Animal I 1 r 12
  • 14. DDL Semantics: Problem with Bridge Rules
    • DDL bridge rules are not compositional :
    • r 13 cannot be inferred from r 12 and r 23
    • Knowledge is not transitively reusable!
    1:Chicken 2:Bird 3:Animal Chicken Animal ?
  • 15. DDL Semantics: Problem with Bridge Rules
    • DDL bridge rules do not preserve concept unsatisfiability across modules
    1: Fly 1: Bird 2:Penguin Bird Penguin ~Fly Penguin
  • 16. E-Connections
    • E-connections allow multiple links between two local domains [Grau, 2005]
    • Links can be used to construct local concepts
    PetOwner Pet PetOwner Pet  owns
  • 17. E-Connections [Grau, 2005]
    • A concept cannot be declared in an ontology as a subclass of a foreign concept;
    • A property cannot be declared as sub-relation of a foreign property;
    • An individual cannot be declared as an instance of a foreign concept;
    • A pair of individuals cannot instantiate a foreign property;
    • The use of E-Connections semantics with owl:imports syntax leads to several difficulties
  • 18. Section summary
    • OWL
      • No localized or contextualized semantics,
      • No partial reuse.
    • DDL
      • Allows inter-module concept inclusions (but not inter-module roles)
      • In general, does not support transitive knowledge reuse or preservation of unsatisfiability
    • E-Connections
      • Allows inter-module roles (but not concept inclusions)
      • Presents strong expressivity limitation
    • P-DL aims to overcome these limitations
  • 19. Outline
    • Introduction
      • Motivation, desiderata and state-of-the-art of modular ontologies
    • Representing Modular Ontology
      • Using Package-based Description Logics (P-DL)
    • Reasoning with Modular Ontology
      • Distributed reasoning in P-DL using tableau algorithm
    • Privacy-Preserving Reasoning with Hidden Knowledge
    • Collaborative Building of Modular Ontologies
  • 20. Package-Based Description Logics (P-DL)
    • P-DL support semantic importing
    O 1 (Animal) O 2 (Pet)
  • 21. Syntax of P-DL i
    • Contextualized negation
      • There is no global negation, but only contextualized negation for each package
      • Example:
    • Package and Importing
    P i Male, Female P j 
  • 22. Semantics of P-DL
    • Localized Semantics
    People Animals O 1 O 2
  • 23. Semantics of P-DL
    • Semantic importing akin to “citation”
    • Package 2 cites package 1 for the definition of ‘1:Dog’
      • Interpretation of ‘1:Dog’ is the same on the shared portions of the local domains of packages 1 and 2
      • The two packages need not agree on the interpretation of other unrelated concepts (e.g., Cats)
    • P-DL supports selective knowledge reuse
    P 1 P 2 1:Dog 2:PetDog 1:Dog
  • 24. Semantics of P-DL
    • Domain relations are composi- tionally consistent
    • r 13 =r 23 O r 12
    • More requirements are needed when importing of roles and nominals are allowed.
    • Importing establishes one-to-one domain relations
    • (1:Dog) I 2 =r 12 (1:Dog I 1 )
    x x’ Δ I 1 Δ I 2 1:Dog I 1 1:Dog I 2 r 12 Δ I 3 r 13 r 23 x’’ 1:Dog I 3
  • 25. Semantics of P-DL
    • Each package witnesses consequences from its own point of view (using its local and imported knowledge)
    importee importer consequences importer consequences ² ²
  • 26. Properties of P-DL
    • Exact Reasoning:
      • extending an ontology in the classic way and in the modular way will ensure same inferential results.
    Integrated ontology Modular ontology Dog Animal Dog Animal
  • 27. Properties of P-DL
    • Directional Relation
    X D E A B A B D E
  • 28. Properties of P-DL
    • The preservation of unsatisfiability
    • Transitive Reusability
    Dog Dog Animal Pet Animal P 1 P 2 P 3 (P j imports P i )
  • 29. P-DL Families
    • P – package extension with importing of any type of names (concept, role and nominal)
      • P - - acyclic importing : if P (directly or indirectly) imports Q, then Q cannot (directly or indirectly) import P
      • P C – importing of concept names only
    • Examples:
      • ALCP C [Bao et al,CRR 2006]
      • ALCP C -[Bao et al,WI 2006]
      • SHIQP [Bao et al,ISWC 2007]
      • SHOIQP [Bao et al,AAAI 2007]
  • 30. DDL and E-connections vs P-DL
    • P-DL can simulate
      • DDL with bridge rules using subsumption between
        • imported concepts and local concepts
        • imported roles and local roles
      • (one-way binary) E-Connections using roles that relate a local concept with an imported concept
    • DDL, E-Connection or their combination cannot simulate P-DL
      • One-to-one domain relations cannot be simulated by DDL or E-Connections
      • P-DL, unlike DDL and E-connections, supports transitive reuse of knowledge
  • 31. Section Summary
  • 32. Section Summary (Details in dissertation Table 4.4) 1,4 Limited Support 2,3 May be simulated using syntactical encoding
  • 33. Outline
    • Introduction
      • Motivation, desiderata and state-of-the-art of modular ontologies
    • Representing Modular Ontology
      • Using Package-based Description Logics (P-DL)
    • Reasoning with Modular Ontology
      • Distributed reasoning in P-DL using tableau algorithm
    • Privacy-Preserving Reasoning with Hidden Knowledge
    • Collaborative Building of Modular Ontologies
  • 34. Tableau Algorithm
    • Description Logics usually uses the Tableau Algorithm [Baader & Sattler 2001] for reasoning tasks.
    • A tableau is a representation of a model
      • A model for an ontology represents a world which satisfies assertions in the ontology.
      • Decidable DLs typically have tree models [Vardi,1996]
    • Tableau algorithms try to check concept satisfiability w.r.t. a KB by constructing a tree that is the model of the concept and the KB
    Ontology: Man ⊑ Human Model: Man Human
  • 35. Tableau Algorithm: Example
    • Dog ⊑ Animal
    • Dog ⊑  eats.DogFood
    • DogFood ⊑  hasTM.Brand
    • DogFood ⊑  soldBy.Supermarket
    Completion Tree (Tableau) Note: the tableau is simplified for demonstration purpose If “Dog” is satisfiable? goofy L(goofy)={Dog, Animal, eats.DogFood } foo L(foo)={DogFood } {eats} pedigree L(pedigree)={Brand } {hasTM} walmart L(walmart)={Supermarket} {soldBy}
  • 36. Reasoning for Modular Ontology
    • Major Considerations:
      • Avoid integrating ontology modules
      • Minimize local memory cost
      • Respect module autonomy, e.g., privacy
    • Question: can we reason with P-DL without
      • (syntactic level) an integrated ontology ?
      • (semantic level) a (materialized) global tableau ?
  • 37. Federated Reasoning
    • There are multiple local reasoners, one for each package
      • Each local reasoner only knows and uses local knowledge
      • A reasoner may ask another reasoner (by messages) about the meaning of imported names .
    What is a “Dog”? “ Dog” is a type of “Animal” Dog Dog ⊑ Animal P 2 P 1
  • 38. Distributed Tableau
    • Distributed tableau
    • each local tableau is a fragment of the virtual global tree
    • thus, each local tableau is a forest
    • a node may be “shared” among local tableaux (indicated by domain relations)
    (Virtual) combined tableau for the (conceptual) integrated ontology from all packages
  • 39. Construction of Distributed Tableau
    • Developed algorithms ALCP C , ALCP C - , SHIQP
    • Basics of the algorithm:
      • Intra-tableau expansion rules : e.g., if C ⊓D  L(x), then {C,D} <= L(x)
      • Inter-tableau expansion rules : e.g., if C  L(x), C is defined in another package P, then send a reporting message r(x,C) to the reasoner of P.
      • Termination : is guaranteed using suitable blocking rules.
      • The algorithm is proven to be sound and complete .
  • 40. Example
    • Check if PetDog is satisfiable as witnessed by O 2
    { other axioms … } O 1 (Animal) O 2 (Pet)
  • 41. Example
    • Each local reasoner maintains a local tableau.
    • Connections between local tableaux is created by a set of messages.
    R 1  2 (x 1 ,Animal) {Animal} R 1  2 (x 2 ,Animal) Note: the tableau is simplified for demonstration purpose PetDog ⊑ Dog PetDog ⊑  eats.DogFood Dog ⊑ Carnivore Carnivore ⊑ Animal Carnivore ⊑  eats.Animal x 1 {PetDog} Local Reasoner 2 (for package Pet) R 1  2 (x 1 ,Dog) {DogFood} x 2 {eats} {eats} x 2 ’ x 1 {Dog,Carnirvore,Animal} ’ Local Reasoner 1 (for package Animal) R 1  2 (<x 1 ,x 2 >,eats) Expansion for other axioms in P Animal
  • 42. Section Summary
    • Distributed reasoning algorithms have been designed for P-DL:
      • Federated : no integration of all ontology modules is required;
      • Peer-to-peer : each local reasoner only requires local knowledge;
      • Parallel : subtasks in reasoning can be explored concurrently by multiple reasoners;
      • Message-based : the overall reasoning process is enabled by messages exchanged between local reasoners.
    • Algorithms available for ALCP C - , ALCP C , SHIQP
  • 43. Outline
    • Introduction
      • Motivation, desiderata and state-of-the-art of modular ontologies
    • Representing Modular Ontology
      • Using Package-based Description Logics (P-DL)
    • Reasoning with Modular Ontology
      • Distributed reasoning in P-DL using tableau algorithm
    • Privacy-Preserving Reasoning with Hidden Knowledge
    • Collaborative Building of Modular Ontologies
  • 44. Partially Hidden Knowledge Locally visible : Has date Globally visible : Has activity Bob’ schedule ontology
  • 45. Privacy-Preserving Reasoning
    • A reasoner should not expose hidden knowledge
    • However, such hidden knowledge may still be (indirectly) used in safe queries.
    Queries Yes Unknown
  • 46. Privacy-Preserving Reasoning
    • Practical algorithms designed for
      • Hierarchical ontologies. (e.g. biological ontologies)
      • Description Logics (e.g. SHIQ)
      • Open for P-DL
    • Applications
      • Privacy protection in medical information system
      • Secure web service
      • Query answering in p2p applications
  • 47. Outline
    • Introduction
      • Motivation, desiderata and state-of-the-art of modular ontologies
    • Representing Modular Ontology
      • Using Package-based Description Logics (P-DL)
    • Reasoning with Modular Ontology
      • Distributed reasoning in P-DL using tableau algorithm
    • Privacy-Preserving Reasoning with Hidden Knowledge
    • Collaborative Building of Modular Ontologies
  • 48. Collaborative Ontology Building
    • Ontology modularity facilitates collaborative building
    • Each package can be independently developed
    • Different curators can concurrently edit the ontology on different packages
    • Ontology can be only partially loaded
    • Unwanted interactions are minimized by limiting term and axiom visibility
    • Prototypes
    • COB-Editor [Bao et al, BIDM 2006]
    • WikiOnt [Bao & Honavar, EON 2004]
  • 49. The COB Editor Pig Package Cattle Package Chicken Package
  • 50. WikiOnt 2 (Ongoing)
  • 51. Contributions Figure courtesy of Tim Berners-Lee, AAAI 2006
    • Formal investigation on requirements of modular ontologies
    • The specification of Package-based Description Logics (P-DL) which overcomes many semantic problems and expressivity limitations of existing approaches
    Chapter 3,4
    • Distributed reasoning algorithms for modular ontologies
    • federated, no integration required
    • peer-to-peer
    • parallel reasoning, scalable for large ontologies
    • message-based
    Chapter 5
    • Privacy-preserving inference with hidden knowledge
    • general framework
    • practical algorithms for hierarchies and DL
    Chapter 6
    • Collaborative Building of Modular Ontologies
    • Software prototypes: WikiOnt and COB-Editor
    Chapter 7
  • 52. Results
    • Presentations
      • Academic Conferences: AAAI-07, RR-07 (Web Reasoning and Rule System) , WI-06 (Web Intelligence) , ISWC-06 (International Semantic Web Conference) , ASWC-06 (Asian Semantic Web Conference, Best Paper )
      • Industrial Conferences: SemGrail (Microsoft) 2007, Semantic Technology Conference 2007
    • Funding
      • Results of this study formed the basis of proposals on modular ontologies that were funded by NSF (IIS-0639230) and ISU CIAG (Center for Integrated Animal Genomics)
    • Community Involvement
      • 4 workshop organization efforts on related topics (SWeCka 2006,2007, Modular Ontologies (WoMo) 2006,2007)
  • 53. Future Work
    • Modular Ontology Framework
      • Understanding modular ontology using DL + rules; RDF modularity
    • Extending P-DL
      • ABox, Query, Syntax, Interfaces and Views
    • Distributed Reasoning
      • Implementation, SHOIQ reasoning, optimization
    • Privacy-Preserving Reasoning
      • P-DL, RDF, medical ontologies
    • Applications
      • WikiOnt2, Semantic Data Integration (INDUS project)
  • 54. Acknowledgement
    • Major advisor : Vasant Honavar
    • Modular Ontology Group : Giora Slutzki, Doina Caragea, George Voutsadakis
    • COB-Editor Group : LaRon Hughes, Zhiliang Hu, Peter Wong, James Reecy,
    • Medical Ontology Building : Yu Cao, Wallapak Tavanapong,
    • INDUS Group : Doina Caragea, Jyotishman Pathak, Neeraj Koul, Jaime Reinoso-Castillo
    • Discussion : Gary Leavens, Dae-ki Kang, Rafael-Armando Jordan, Adrian Silvescu, Kewei Tu, Jun Zhang, Feihong Wu, Changhui Yan, Hua Pei, Hua Ming, and other members of the AI Lab.
    • Non-ISU collaboration : Jeff Pan, Yimin Wang, Luciano Serafini, Andrei Tamilin, Zhengxiang Pan and Jing Mei.
    • Research supported by funding from National Science Foundation (IIS 0219699,0639230),National Institutes of Health (GM 066387), and Center for Integrated Animal Genomics, Iowa State University, and grants from USDA NAGRP Bioinformatics Coordination Project.
  • 55.
    • Backup
  • 56. Why not owl:imports?
    • owl:imports does not preserve semantics of imported concepts or roles as defined in the source ontology (loss of context)
    • owl:imports does not support partial reuse
  • 57. Hidden Knowledge vs. Incomplete Knowledge
    • Open World Assumption (OWA)
    • An ontology may have only incomplete knowledge about a domain
      • KB: Dog is Animal
      • Query: if Cat is Animal ? Unknown if Cat is not Animal ? Also unknown
    • Hidden knowledge can be protected as if it is incomplete knowledge
  • 58. Privacy-Preserving Reasoner
    • A privacy-preserving reasoner should be
      • History independent: it answers in the same way regardless the history of past queries
      • Honest: it never “lies”
      • History safe: answers and visible knowledge combined cannot be used to infer hidden knowledge
    q R A  {Y,N,U} KB q R KB false
  • 59. Example: Hierarchies 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 )
  • 60. Example: Hierarchies a b c d e Y Y “ unsafe” graph “ safe” graph Reasoning Strategy: Safety Scope: a b c d e
  • 61. Privacy-preserving reasoning with DL
    • Critical visible knowledge (K vc ) contains existing knowledge about Sig(K h )
    • If we can ensure K v + Q Y will not give extra information about Sig(K h ), other than that K vc , then the reasoner is safe
    • Conservative Extension [Grau etal, 2006] :  α of Sig(K vc ), K vc |= α iff K v +Q Y |= α
    • Practical algorithm exists for SHIQ (using “local ontologies” [Grau et al, IJCAI 2007] )
    Hidden knowledge (K h ) Visible knowledge (K v ) Critical visible knowledge (K vc ) C ⊑ D C ⊑  R. D G ⊑ H axioms that contain names in Sig(K h )
  • 62. Privacy-preserving reasoning with P-DL
    • Still an open problem
    • Key issue: message safety
    r(x,Dog), r(x,  Animal)  Dog ⊑ Animal P 1 Dog ⊑ Animal inferred!
  • 63. Section Summary
    • Selective knowledge reuse using partially hidden knowledge
    • Privacy-preserving reasoning based on the open world assumption
    • Practical algorithms available for hierarchies and DL SHIQ.
  • 64. WikiOnt
    • A web browser based ontology editor
    • Using Wiki script to store ontologies
    • With features to support team work, version control, page locking, and navigation.

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