2 ontologies I

647 views

Published on

Knowledge & Media 2012 Lecture 2

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
647
On SlideShare
0
From Embeds
0
Number of Embeds
203
Actions
Shares
0
Downloads
18
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

2 ontologies I

  1. 1. Ontology construction I Knowledge & Media 2012 Lecture 2(adapted from Ontology Engineering 2011)
  2. 2. Overview•  Subclass relations•  Reflections on category representations –  Levels in hierarchies –  Sets versus prototypes•  Construction patters –  N-ary relations –  Value sets versus value partitions 2
  3. 3. Subclass relation in UML 3
  4. 4. Multiple inheritance 4
  5. 5. Generalization properties•  Completeness {complete} = each object participates in AT LEAST one subclass {incomplete} = subclass participation is optional (use, e.g. with single subclasses)•  Disjointedness {disjoint} = object participates in AT MOST one subclass {overlapping} = object may belong to multiple subclasses 5 “multiple specialization”
  6. 6. Two different organizationsof the disease hierarchy 6
  7. 7. viewpoints - simultaneousmultiple classifications 7
  8. 8. Limitations of Hierarchies•  What’s in a link? –  Hierarchical links often have different semantics•  “Dimensions” of distinction making provide rationale for hierarchical levels –  (Multiple) classification along different dimensions within single hierarchy creates confusion and makes applications unnecessarily complex•  Hierarchy enforces a single fixed sequence of dimensions –  fixed ordering not always possible or desirable 8
  9. 9. Categorization•  OWL (Description logic) takes an extensional view of classes –  A set is completely defined by its members•  This puts the emphasis on specifying class boundaries•  Work of Rosch et al. takes a different view 9
  10. 10. Categories (Rosch)•  Help us to organize the world•  Tools for perception•  Basic-level categories –  Are the prime categories used by people –  Have the highest number of common and distinctive attributes –  What those basic-level categories are may depend on context 10
  11. 11. Basic-level categories 11
  12. 12. Vertical organization ofhierarchies•  Basic-level classes often occur as a middle layer in hierarchies•  Higher levels: abstract classes that organize the hierarchy•  Lower levels: domain/context specific classes –  may require particular expertise to understand 12
  13. 13. Class room exercise•  Study the hierarchy of “chairs” in the Art & Architecture Thesaurus http://www.getty.edu/research/tools/ vocabularies/aat/•  Check whether this hierarchy follows the pattern described by Rosch 13
  14. 14. Horizontal organization ofcategories•  Categories at the same level of abstraction•  People use prototypes to characterize these –  Some chairs are more typically “chair” than others•  Emphasis is more on what is common for a category than on differences with other categories 14
  15. 15. Construction patterns: Representing n-ary relations
  16. 16. Re-representing properties asclasses•  To say something about a property it must be re- represented as a class –  property: hasDanger à Class: Danger •  plus properties of Danger: hasReason hasRisk hasAvoidanceMeasure –  Sometimes called “reification” •  But “reification” is used differently in different communities
  17. 17. Pattern 1: dependent values•  Relation between two concepts•  One of the concepts can have multiple features that depend on the relation•  Example: diagnosis of a disease with a certain confidence level
  18. 18. Pattern 1: example instance
  19. 19. Pattern 1: example instancein RDF:Christine
 a :Person ;
 :has_diagnosis _:Diagnosis_Relation_1 .

:_Diagnosis_relation_1
 a :Diagnosis_Relation ;
 :diagnosis_probability :HIGH;
 :diagnosis_value :Breast_Tumor_Christine .
  20. 20. Pattern 1: class constraints
  21. 21. Pattern 1: class constraints in RDF:Diagnosis_Relation" a owl:Class ;" rdfs:subClassOf" [ a owl:Restriction ;" owl:someValuesFrom :Disease ;" owl:onProperty :diagnosis_value" ] ;" rdfs:subClassOf" [ a owl:Restriction ;" owl:allValuesFrom :Probability_values ;" owl:onProperty :diagnosis_probability" ] ."":Person" a owl:Class ;" rdfs:subClassOf" [ a owl:Restriction ;" owl:allValuesFrom :Diagnosis_Relation ;" owl:onProperty :has_diagnosis" ] ."
  22. 22. Pattern 2: relation as class•  The relation itself is a concept•  All arguments are equally important•  Examples: –  Enrollment –  Transaction –  Purchase –  Clue (the butler with the rope in the kitchen)•  See also the notion of UML association class
  23. 23. Association class
  24. 24. Pattern 2: example instance
  25. 25. Pattern 2: example instance inRDF:Purchase_1" a :Purchase ;" :has_buyer :John ;" :has_object :Lenny_The_Lion ;" :has_purpose :Birthday_Gift ;" :has_amount 15 ;" :has_seller :books.example.com ."
  26. 26. Pattern 2: class constraints
  27. 27. Pattern 2: class constraints inRDF:Purchase" a owl:Class ;" rdfs:subClassOf" [ a owl:Restriction ;" owl:allValuesFrom :Purpose ;" owl:onProperty :has_purpose" ] ;" rdfs:subClassOf" [ a owl:Restriction ;" owl:cardinality 1 ;" owl:onProperty :has_buyer" ] ;" rdfs:subClassOf" [ a owl:Restriction ;" owl:onProperty :has_buyer ;" owl:someValuesFrom :Person" ] ;"..."
  28. 28. Literature•  http://www.w3.org/TR/swbp-n- aryRelations/ 28
  29. 29. Construction patterns: Specifying value sets
  30. 30. Specifying value sets  Modifiers   Domestication   Domestic •  Identify modifiers that are   Wild mutually exclusive   Feral   Risk –  Domestication Dangerous     Risky –  Risk Safe     Sex –  Sex     Male Female –  Age   Age   Child •  Make meaning precise   Infant –  Age  Age_group   Toddler   Adult   Elderly 30
  31. 31. Options for representingvalue sets•  Symbolic values –  Individuals that enumerate all states of a Quality •  The enumeration of the values equals the quality class•  Value partitions –  Classes that partition a Quality •  The disjunction of the partition classes equals the quality class 31
  32. 32. Value sets for specifyingvalues•  A quality – SexValue•  Individuals for each value –  male, female•  Values all different (NOT assumed by OWL)•  Value type is enumeration of values SexValue = {male, female}•  A functional property hasSex MaleAnimal = Animal and hasSex is male 32
  33. 33. Value Partitions:example Age Group•  How to represent the values for Age Group?•  Option: –  specify Child, Toddler, etc. as subclasses of AgeGroup –  Specify age-group values as instances of the relevant age-group class ex:MyAgeGroup rdf:type ex:Adult .•  Main advantage: flexibility 34
  34. 34. Issues in specifying values•  Value Partitions –  Can be subdivided and specialised –  Fit with philosophical notion of a quality space (cf. e.g. DOLCE) –  Require interpretation to go in databases as values •  in theory but rarely considered in practice –  Work better with existing classifiers in OWL-DL•  Value Sets –  Cannot be subdivided –  Fit with intuitions –  More similar to databases – no interpretation –  Work less well with existing classifiers 37
  35. 35. Class room exercise•  Assume the following use case: for the collection of a museum we need to describe the color of clothes. These clothes can have subtle color variations, so we need an extensive color vocabulary. The museum uses the Art and Architecture Thesaurus for describing the items in their collections. This thesaurus contains extensive information about colors.•  Your task is to specify the values that a property "hasColor" can take for the class "Cloth". AAT contains more than 200 colors, but you can limit yourself to a representative subset of purple colors (at least 2 layers of ancestors below <purple color>).The subset should allow you to specify the relevant distinctions you want to make. 38
  36. 36. Literature•  http://www.w3.org/TR/swbp-specified- values/ 39

×