Ch. 13 of the CommonKADS textbook

1. Advanced Knowledge Modeling
Additional domain constructs
Domain-knowledge sharing and reuse
Catalog of inferences
Flexible use of task methods

2. Advanced knowledge modelling 2
Viewpoints
■ need for multiple sub-type hierarchies
■ sub-type-of = "natural" sub-type dimension
➤ typically complete and total
■ other sub-type dimensions: viewpoint
➤ represent additional ways of "viewing" a certain concept
■ similar to UML "dimension"
■ helps to introduce new vocabulary through multiple
specialization ("inheritance")

3. Advanced knowledge modelling 3
Two different organizations of
the disease hierarchy
infection
meningitis pneumonia
bacterial
pneumonia
acute
viral
pneumonia
chronic
viral
pneumonia
viral
pneumonia
infection
meningitis pneumonia
chronic
pneumonia
acute
viral
pneumonia
acute
bacterial
pneumonia
acute
pneumonia

4. Advanced knowledge modelling 4
Viewpoint specification
concept infection;
super-type-of: meningitis, pneumonia;
viewpoints:
time-factor:
acute-infection, chronic-infection;
causal-agent:
viral-infection, bacterial-infection;
end concept infection;
concept acute-viral-meningitis;
sub-type-of: meningitis, acute-infection, viral-infection;
end concept acute-viral-meningitis;

5. Advanced knowledge modelling 5
Viewpoint:
graphical representation
infection
acute
infection
chronic
infection
viral
infection
bacterial
infection
mening itispneumonia
acute
viral
mening itis
causal
agent
time
factor

6. Advanced knowledge modelling 6
Expressions and Formulae
■ need for expressing mathematical models or logical
formulae
■ imported language for this purpose
➤ Neutral Model Format (NMF)
■ used in technical domains
■ see appendix

7. Advanced knowledge modelling 7
Rule instance format
■ See appendix for semi-formal language
■ Guideline: use what you are comfortable with
■ May use (semi-)operational format, but for conceptual
purposes!
■ Implicit assumption: universal quantification
➤ person.income < 10.000 suggests loan.amount < 1.000
➤ “for all instances of person with an income less than 10.00
the amount of the loan should not exceed 1.000

8. Advanced knowledge modelling 8
Inquisitive versus formal rule
representation
Intuitive rule representation
residence-application.applicant.household-type = single-person
residence-application.applicant.age-category = up-to-22
residence-application.applicant.income < 28000
residence-application.residence.rent < 545
INDICATES
rent-fits-income.truth-value = true;
Formal rule representation
FORALL x:residence-application
x.applicant.household-type = single-person
x.applicant.age-category = up-to-22
x.applicant.income < 28000
x,residence.rent < 545
INDICATES
rent-fits-income.truth-value = true;

9. Advanced knowledge modelling 9
Using variables in rules to
eliminate ambiguities
/* ambiguous rule */
employee.smoker = true AND
employee.smoker = false
IMPLIES-CONFLICT
smoker-and-non-smoker.truth-value =true;
/* use of variables to remove the ambiguity */
VAR x, y: employee;
x.smoker = true AND
y.smoker = false
IMPLIES-CONFLICT
smoker-and-non-smoker.truth-value =true;

10. Advanced knowledge modelling 10
Constraint rules
■ Rules about restrictions on a single concept
■ No antecedent or consequent
component
component
constraint
R ULE -­‐T Y P E
component-­‐constraint;
C ONS T R AINT :
component;
E ND
R ULE -­‐T Y P E
component-­‐constraint;
E xample
constraints
(car
is
a
component):
car.weight
<
500
kg
car.length
<
5.5
m

11. Advanced knowledge modelling 11
Knowledge sharing and reuse:
why?
■ KE is costly and time-consuming
➤ general reuse rationale: quality, etc
■ Distributed systems
➤ knowledge base partitioned over different locations
■ Common vocabulary definition
➤ Internet search, document indexing, ….
➤ Cf. thesauri, natural language processing
■ Central notion: “ontology”

12. Advanced knowledge modelling 12
The notion of ontology
■ Ontology =
explicit specification of a shared
conceptualization that holds in a particular
context”
(several authors)
■ Captures a viewpoint an a domain:
➤ Taxonomies of species
➤ Physical, functional, & behavioral system descriptions
➤ Task perspective: instruction, planning

13. Advanced knowledge modelling 13
Ontology should allow for
“representational promiscuity”
ontology
parameter
constraint -expression
knowledge base A
cab.weight + safety.weight
= car.weight:
cab.weight < 500:
knowledge base B
parameter(cab.weight)
parameter(safety.weight)
parameter(car.weight)
constraint-expression(
cab.weight + safety.weight
= car.weight)
constraint-expression(
cab.weight < 500)
rewritten as
viewpoint
mapping rules

14. Advanced knowledge modelling 14
Ontology types
■ Domain-oriented
➤ Domain-specific
– Medicine => cardiology => rhythm disorders
– traffic light control system
➤ Domain generalizations
– components, organs, documents
■ Task-oriented
➤ Task-specific
– configuration design, instruction, planning
➤ Task generalizations
– problems solving, e.g. UPML
■ Generic ontologies
– “Top-level categories”
– Units and dimensions

15. Advanced knowledge modelling 15
Using ontologies
■ Ontologies needed for an application are typically a
mix of several ontology types
➤ Technical manuals
– Device terminology: traffic light system
– Document structure and syntax
– Instructional categories
➤ E-commerce
■ Raises need for
➤ Modularization
➤ Integration
– Import/export
– Mapping

16. Advanced knowledge modelling 16
Domain standards and
vocabularies as ontologies
■ Example: Art and Architecture Thesaurus (AAT)
■ Contain ontological information
➤ AAT: structure of the hierarchy
■ Ontology needs to be “extracted”
➤ Not explicit
■ Can be made available as an ontology
➤ With help of some mapping formalism
■ Lists of domain terms are sometimes also called “ontologies”
➤ Implies a weaker notion of ontology
➤ Scope typically much broader than a specific application domain
➤ Example: domain glossaries, WordNet
➤ Contain some meta information: hyponyms, synonyms, text

17. Advanced knowledge modelling 17
Ontology specification
■ Many different languages
➤ KIF
➤ Ontolingua
➤ Express
➤ LOOM
➤ UML
➤ ......
■ Common basis
➤ Class (concept)
➤ Subclass with inheritance
➤ Relation (slot)

18. Advanced knowledge modelling 18
Additional expressivity (1 of 2)
■ Multiple subclasses
■ Aggregation
➤ Built-in part-whole representation
■ Relation-attribute distinction
➤ “Attribute” is a relation/slot that points to a data type
■ Treating relations as classes
➤ Sub relations
➤ Reified relations (e.g., UML “association class”)
■ Constraint language
➤ First-order logic
➤ Second-order statements

19. Advanced knowledge modelling 19
Additional expressivity (2 of 2)
■ Class/subclass semantics
➤ Primitive vs. defined classes
➤ Complete/partial, disjoint/overlapping subclasses
■ Set of basic data types
■ Modularity
➤ Import/export of an ontology
■ Ontology mapping
➤ Renaming ontological elements
➤ Transforming ontological elements
■ Sloppy class/instance distinction
➤ Class-level attributes/relations
➤ Meta classes

20. Advanced knowledge modelling 20
Priority list for expressivity
■ Depends on goal:
➤ Deductive capability: “limit to first-order logic”
➤ Maximal content: “as much as (pragmatically) possible”
■ My priority list (from a “maximal-content” representative)
1. Multiple subclasses
2. Reified relations
3. Import/export mechanism
4. Sloppy class/instance distinction
5. (Second-order) constraint language
6. Aggregation

21. Advanced knowledge modelling 21
Art & Architecture Thesaurus
Used for
indexing
stolen art
objects in
European
police
databases

22. Advanced knowledge modelling 22
The AAT ontology
description
universe
description
dimension
descriptor
value set
value
descriptor
value
object
object type object class
class
constraint
has feature
descriptor
value set
in dimension
instance of
class of
has
descriptor
1+
1+
1+
1+
1+
1+

23. Advanced knowledge modelling 23
Document fragment ontologies:
instructional

24. Advanced knowledge modelling 24
Domain ontology of a traffic
light control system

25. Advanced knowledge modelling 25
Two ontologies of document
fragments

26. Advanced knowledge modelling 26
Ontology for e-commerce

27. Advanced knowledge modelling 27
Top-level categories:
many different proposals
Chandrasekaran et al. (1999)

28. Advanced knowledge modelling 28
Catalog of inferences
■ Inferences are key elements of knowledge models
➤ building blocks
■ No theory of inference types
➤ see literature
■ CommonKADS: catalog of inferences used in
practice
➤ guideline: maintain your own catalog

29. Advanced knowledge modelling 29
Catalog structure
■ Inference name
■ Operation
➤ input/output features
■ Example usage
■ Static knowledge
➤ features of domain knowledge required
■ Typical task types
➤ in what kind of tasks can one expect this inference

30. Advanced knowledge modelling 30
Catalog structure (continued)
■ Used in template
➤ reference to template in the CK book
■ Control behavior
➤ does it always produce a solution?
➤ can it produce multiple solutions?
■ Computation methods
➤ typical algorithms for realizing the inference
■ Remarks

31. Advanced knowledge modelling 31
Inference “abstract”
■ Operation: input =data set, output= new given
■ Example: medical diagnosis: temperature > 38 degrees is
abstracted to “fever”
■ Static knowledge: abstraction rules, sub-type hierarchy
■ Typical task types: mainly analytic tasks
■ Operational behavior: may succeed more than once.
■ Computational methods: Forward reasoning, generalization
■ Remarks:. Make sure to add any abstraction found to the data
set to allow for chained abstraction.

32. Advanced knowledge modelling 32
Inference “cover”
■ Operation: given some effect, derive a system state that could
have caused it
■ Example: cover complaints about a car to derive potential
faults.
■ Static knowledge: uses some sort of behavioral model of the
system being diagnosed. A causal network is most common. e.
■ Typical task types: specific for diagnosis.
■ Control behavior: produces multiple solutions for same input.
■ Computational methods: abductive methods, ranging from
simple to complex, depending on nature of diagnostic method
■ Remarks: cover is an example of a task-specific inference. Its
use is much more restricted than, for example, the select
inference.

33. Advanced knowledge modelling 33
Multiple methods for a task
■ Not always possible to fix the choice of a method for
a task
➤ e.g. choice depends on availability of certain data
■ Therefore: need to model dynamic method selection
■ Work-around in CommonKADS
➤ introduce method-selection task

34. Advanced knowledge modelling 34
Dealing with dynamic
method selection
associative
generation
generate
hypothesis
model-­‐based
generation
generation
strategy
heuristic
match
causal
covering
generate
hypothesis
causal
covering
single
method
for
hypothesis
generation
work-­‐around
for
multiple
methods
for
the
same
task
obtain
nature
of
data

35. Advanced knowledge modelling 35
Strategic knowledge
■ Knowledge about how to combine tasks to reach a
goal
➤ e.g. diagnosis + planning
■ If complex: model as separate reasoning process!
➤ meta-level planning task