1. Ontology construction I
Course Ontology Engineering

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. Subclass relation in UML
3

4. Multiple inheritance
4

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. Two different organizations
of the disease hierarchy
6

7. viewpoints - simultaneous
multiple classifications
7

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. 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. 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. Basic-level categories
11

12. Vertical organization of
hierarchies
• 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. Class room exercise
• Study the hierarchy of “chairs” in the Art &
Architecture Thesaurus
http://www.getty.edu/research/tools/vocabularies
• Check whether this hierarchy follows the
pattern described by Rosch
13

14. Horizontal organization of
categories
• 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. Construction patterns:
Representing n-ary relations

16. Re-representing properties as
classes
• To say something about a property it must be re-
represented as a class
– property: hasDanger  Class: Danger
• plus properties of Danger:
hasReason
hasRisk
hasAvoidanceMeasure
– Sometimes called “reification”
• But “reification” is used differently in different communities

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. Pattern 1: example instance

19. Pattern 1: example instance
in RDF
:Christine
a :Person ;
:has_diagnosis _:Diagnosis_Relation_1 .
:_Diagnosis_relation_1
a :Diagnosis_Relation ;
:diagnosis_probability :HIGH;
:diagnosis_value :Breast_Tumor_Christine .

20. Pattern 1: class constraints

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. 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. Association class

24. Pattern 2: example instance

25. Pattern 2: example instance in
RDF
: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. Pattern 2: class constraints

27. Pattern 2: class constraints in
RDF
: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. Literature
• http://www.w3.org/TR/swbp-n-aryRelations/
28

29. Construction patterns:
Specifying value sets

30. Specifying value sets
 Modifiers
 Domestication • Identify modifiers that are
 Domestic
 Wild mutually exclusive
 Feral
 Risk – Domestication
Dangerous
– Risk

 Risky


Sex
Safe
– Sex


Male
Female
– Age
 Age • Make meaning precise
 Child
 Infant – Age  Age_group
 Toddler
 Adult
 Elderly
30

31. Options for representing
value 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. Value sets for specifying
values
• 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

34. 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

37. 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

38. 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

39. Literature
• http://www.w3.org/TR/swbp-specified-values/
39