Onto gov

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Onto gov

  1. 1. Foundational Ontology, Conceptual Modeling and Data Semantics Giancarlo Guizzardi gguizzardi@acm.org http://nemo.inf.ufes.br Computer Science Department Federal University ofGT OntoGOV (W3C Brazil), Espírito Santo (UFES), São Paulo, Brazil Brazil
  2. 2. http://nemo.inf.ufes.br/
  3. 3. http://www.inf.ufrgs.br/cita2011/
  4. 4. http://www.inf.ufrgs.br/ontobras-most2011/
  5. 5. http://iaoa.org/
  6. 6. The Dodd-Frank Wall Street Reform and Consumer Protection Act(``Dodd-Frank Act‘) was enacted on July 21, 2010. The Dodd-FrankAct, among other things, mandates that the Commodity FuturesTrading Commission (``CFTC) and the Securities and ExchangeCommission (``SEC) conduct a study on ``the feasibility of requiringthe derivatives industry to adopt standardized computer-readablealgorithmic descriptions which may be used to describe complexand standardized financial derivatives. These algorithmicdescriptions should be designed to ``facilitate computerizedanalysis of individual derivative contracts and to calculate netexposures to complex derivatives. The study also must considerthe extent to which the algorithmic description, ``together withstandardized and extensible legal definitions, may serve as thebinding legal definition of derivative contracts.‘
  7. 7. 7. Do you rely on a discrete set of computer-readable descriptions(``ontologies) to define and describe derivatives transactions andpositions? If yes, what computer language do you use?8. If you use one or more ontologies to define derivativestransactions and positions, are they proprietary or open to thepublic? Are they used by your counterparties and others in thederivatives industry?9. How do you maintain and extend the ontologies that you use todefine derivatives data to cover new financial derivative products?How frequently are new terms, concepts and definitions added?10. What is the scope and variety of derivatives and theirpositions covered by the ontologies that you use? What do theydescribe well, and what are their limitations?.
  8. 8. SEMANTIC INTEROPERABILITY: THEPROBLEM
  9. 9. “What are ontologies and why we need them?”1. Reference Model of Consensus to support different types of Semantic Interoperability Tasks2. Explicit, declarative and machine processable artifact coding a domain model to enable efficient automated reasoning
  10. 10. M
  11. 11. Type ObjectType Sortal Type Mixin TypeRigid Sortal Type Anti-Rigid Sortal Type Anti-Rigid MixinType Kind Phase Role RoleMixin
  12. 12. ? Type ObjectType Sortal Type Mixin TypeRigid Sortal Type Anti-Rigid Sortal Type Anti-Rigid MixinType Kind Phase Role RoleMixin
  13. 13. Type ObjectType Sortal Type Mixin TypeRigid Sortal Type Anti-Rigid Sortal Type Anti-Rigid MixinType Kind Phase Role RoleMixin
  14. 14. R ? Type ObjectType Sortal Type Mixin TypeRigid Sortal Type Anti-Rigid Sortal Type Anti-Rigid MixinType Kind Phase Role RoleMixin
  15. 15. R R’ Type ObjectType Sortal Type Mixin TypeRigid Sortal Type Anti-Rigid Sortal Type Anti-Rigid MixinType Kind Phase Role RoleMixin
  16. 16. Situations represented by Admissible state of affairsthe valid specifications of according to a language L conceptualization C
  17. 17. State of affairs represented by the valid models of Ontology O1 State of affairs represented by the valid models of Ontology O2 Admissible state of affairs according to the conceptualization underlying O2Admissible state of affairsaccording to the conceptualization underlyingO1
  18. 18. State of affairs represented by the valid models of Ontology O1 State of affairs represented by the valid models of Ontology O2 Admissible state of affairs according to the conceptualization underlying O2Admissible state of affairsaccording to the conceptualization underlyingO1 FALSE AGREEMENT!
  19. 19. “one of the main reasons that so many online market makers have foundered [is that] the transactions they had viewed as simple and routine actually involved many subtle distinctions in terminology and meaning” (Harvard Business Review)
  20. 20. 1. We need to recognize that There is not Silver Bullet! and start seing ontology engineering from an engineering perspective
  21. 21. A Software Engineering view… Conceptual Modeling Implementation1 Implementation2 Implementation3
  22. 22. A Software Engineering view… Conceptual Modeling DESIGN Implementation1 Implementation2 Implementation3
  23. 23. …transported to Ontological Engineering Ontology as a Conceptual Model Ontology as Ontology as Ontology as Implementation1 Implementation2 Implementation3 (SHOIN/OWL-DL, (CASL) (Alloy, F-Logic…) DLRUS)
  24. 24. …transported to Ontological Engineering Ontology as a Conceptual Model DESIGN Ontology as Ontology as Ontology as Implementation1 Implementation2 Implementation3 (SHOIN/OWL-DL, (CASL) (Alloy, F-Logic…) DLRUS)
  25. 25. Semantic Networks(Collins & Quillian, 1967)
  26. 26. KL-ONE (Brachman, 1979)
  27. 27. The Logical Level∃x Apple(x) ∧ Red(x)
  28. 28. The Epistemological Level Apple Red color = red sort = apple
  29. 29. The Ontological Level sortal universal characterizing Universal Apple Red color = red sort = apple
  30. 30. Formal Ontology• To uncover and analyze the general categories and principles that describe reality is the very business of philosophical Formal Ontology• Formal Ontology (Husserl): a discipline that deals with formal ontological structures (e.g. theory of parts, theory of wholes, types and instantiation, identity, dependence, unity) which apply to all material domains in reality.
  31. 31. Foundational Ontology• We name a foundational ontology the product of the discipline of formal ontology in philosophy• A foundational ontology is a formal framework of generic (i.e. domain independent) real-world concepts that can be used to talk about material domains.
  32. 32. represented by ConceptualFoundational Ontology Modeling interpreted as Language
  33. 33. Cognitive represented by Foundational UMLOntology (UFO) interpreted as
  34. 34. 2. We need ontologyrepresentations languages which are based on Truly Ontological Distinctions
  35. 35. Formal Relations 0 Weight Quality Dimension w1 w2 heavier (Paul, John)? Paul John
  36. 36. Material Relations 1..* treated In 1..* «role» «kind» Patient Medical Unit
  37. 37. Material RelationsHow are these cardinality constraints to be interpreted ? In a treatment, a patient is treated by several medical units, and a patient can participate in many treatments In a treatment, a patient is treated by several medical units, but a patient can only participate in one treatment In a treatment, several patients can be treated by one medical unit, and a medical unit can participate in many treatments In a treatment, a patient is treated by one medical unit, and a patient can participate in many treatments ...
  38. 38. The problem is even worse in n-ary associations (with n > 2)
  39. 39. Explicit Representation for Material Relations «mediation» «relator» «mediation» Treatment 1..* 1..* 1 1..* «material» /TreatedIn Patient MedicalUnit 1..* 1..*
  40. 40. Material RelationsAs seen before from a relator and mediation relation we can derive several material relationsAsides from all the benefits previously mentioned, perhaps the most important contribution of explicitly considering relations is to force the modeler to answer the fundamental question of what is truthmaker of that relation
  41. 41. Material Relations Yet another example: Modeling that a graduate student have one or more supervisors and a supervisor can supervise one or more students
  42. 42. Material Relations Yet another example: Modeling that a graduate student have one or more supervisors and a supervisor can supervise one or more students
  43. 43. Unified Foundational Ontology (UFO) UFO-C (SOCIAL ASPECTS) (Agents, Intentional States, Goals, Actions, Norms, Social Commitments/Claims, Social Dependency Relations…) UFO-A (STRUCTURAL ASPECTS) UFO-B (DYNAMIC ASPECTS)(Objects, their types, their parts/wholes, (Events and their parts, the roles they play, Relations between events, their intrinsic and relational properties Object participation in events, Property value spaces…) Temporal properties of entities, Time…)
  44. 44. 3. We need Patterns - Design Patterns - Analysis Patterns- Transformation Patterns - Patterns Languages
  45. 45. Roles with Disjoint Allowed Types «role»Customer Person Organization
  46. 46. Roles with Disjoint Allowed Types Person Organization «role»Customer
  47. 47. participation Participant Forum 1..* *Person SIG
  48. 48. Roles with Disjoint Admissible Types «roleMixin» Customer
  49. 49. Roles with Disjoint Allowed Types «roleMixin» Customer «role» «role» PersonalCustomer CorporateCustomer
  50. 50. Roles with Disjoint Allowed Types «roleMixin» Customer Person Organization «role» «role» PersonalCustomer CorporateCustomer
  51. 51. «roleMixin» «kind» «roleMixin» «kind» Customer Social Being Participant Social Being«kind» Organization «kind» SIGPerson Person «role» «role» «role» «role» PrivateCustomer CorporateCustomer IndividualParticipant CollectiveParticipant
  52. 52. Roles with Disjoint Admissible Types 1..* F «roleMixin» 1..* A D E «role» «role» B C
  53. 53. Quality, Quality Values and Quality Dimensions c::Color c v2 Color Quality Space a wa::Apple w::Weight v1 0 Weight Quality Space
  54. 54. Representing Qualities and QualityStructures Explicitly
  55. 55. Representing Qualities and QualityStructures Explicitly HSBColorDomain i <h1,s1,b1> a::Apple c::Color equivalence RGBColorDomain <r1,g1,b1>
  56. 56. John part-ofpart-of part-of
  57. 57. John’s Brain part-of John part-ofpart-of
  58. 58. enrolled atStudent Course 20..* 11 1 representative for
  59. 59. 4. We need tools to create, verify, validate and handle the complexity of the produced models
  60. 60. NamedElement 1..* /relatedElement Type name:String[0..1] Element /source 1..* 1..* /target specific * Classifier Relationship isAbstract:Boolean = false 1 /general 1 general DirectedRelationship Class generalization Generalization * GeneralizationSet * *isCovering:Boolean = falseisDisjoint:Boolean = true Object Class {disjoint, complete} Sortal Class Mixin Class {disjoint, complete} {disjoint, complete} Rigid Sortal Class Anti Rigid Sortal Class Rigid Mixin Class Non Rigid Mixin Class {disjoint, complete} {disjoint, complete} {disjoint, complete} Anti Rigid Mixin Class Semi Rigid Mixin Substance Sortal SubKind Phase Role Category RoleMixin Mixin {disjoint, complete} CollectiveKind Quantity isExtensional:Boolean
  61. 61. Tool Support The underlying algorithm merely has to check structural properties of the diagram and not the content of involved nodes
  62. 62. 1..* «mediation» «role»Transplant Surgeon «kind» «role»Organ Donee Person 1 «mediation» 1..* «mediation»«role»Organ Donor «relator»Transplant 1 1..* 1..*
  63. 63. ATL Transformation Simulation and VisualizationAlloy Analyzer + OntoUML visual Plugin
  64. 64. SEMANTIC INTEROPERABILITY: THEPROBLEM REVISITED
  65. 65. representation interpretation M
  66. 66. semantic distance (δ)representation interpretation M
  67. 67. when δ < x then we consider the communication to be effective, i.e., we assume theexistence of single shared conceptualization semantic distance (δ) representation interpretation M
  68. 68. δ R R’ Type ObjectType Sortal Type Mixin TypeRigid Sortal Type Anti-Rigid Sortal Type Anti-Rigid MixinType Kind Phase Role RoleMixin
  69. 69. ConsistentIntegrated Use
  70. 70. Small δ, Small Ontology Big δ, Small Ontology Well-Founded Techniches Matching & Alignment Techniches Small δ, Big Ontology Big δ, BIg Ontology
  71. 71. Small δ, Small Ontology Big δ, Small Ontology Some Flexibility Well-Founded Techniches Matching & Alignment Techniches Small δ, Big Ontology Big δ, BIg Ontology
  72. 72. Small δ, Small Ontology Big δ, Small Ontology Some Flexibility Well-Founded Techniches Matching & Alignment Techniches Intractable! Small δ, Big Ontology Big δ, BIg Ontology
  73. 73. State of affairs represented by the valid models of Ontology O1 State of affairs represented by the valid models of Ontology O2 Admissible state of affairs according to the conceptualization underlying O2Admissible state of affairsaccording to the conceptualization underlying FOUNDATIONALO1 ONTOLOGY
  74. 74. The alternative to ontology isnot “non-ontology” but badontology!
  75. 75. http://nemo.inf.ufes.br/ gguizzardi@inf.ufes.br

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