Advanced Knowledge Modeling

1
Advanced Knowledge Modeling

• Additional domain constructs
• Domain-knowledge sharing and reuse
• Catalog of inferences
• Flexible use of task methods

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
Two different organizations of the disease
hierarchy
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
Viewpoint graphical representation
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
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 lt 10.000 suggests loan.amount lt
  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
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 lt 28000
• residence-application.residence.rent lt 545
• INDICATES
• rent-fits-income.truth-value true
• Formal rule representation
• FORALL xresidence-application
• x.applicant.household-type
  single-person
• x.applicant.age-category up-to-22
• x.applicant.income lt 28000
• x,residence.rent lt 545
• INDICATES
• rent-fits-income.truth-value true

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
Constraint rules

• Rules about restrictions on a single concept
• No antecedent or consequent

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
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
Ontology should allow for representational
promiscuity
ontology
parameter
constraint -expression
mapping rules
viewpoint
knowledge base B
knowledge base A
parameter(cab.weight)
parameter(safety.weight)
cab.weight safety.weight
parameter(car.weight)
rewritten as
car.weight
constraint-expression(
cab.weight safety.weight
cab.weight lt 500
car.weight)
constraint-expression(
cab.weight lt 500)
14
Ontology types

• Domain-oriented
• Domain-specific
• Medicine gt cardiology gt 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
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
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
Ontology specification

• Many different languages
• KIF
• Ontolingua
• Express
• LOOM
• UML
• ......
• Common basis
• Class (concept)
• Subclass with inheritance
• Relation (slot)

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
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
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)
• Multiple subclasses
• Reified relations
• Import/export mechanism
• Sloppy class/instance distinction
• (Second-order) constraint language
• Aggregation

21
Art Architecture Thesaurus
Used for indexing stolen art objects in
European police databases
22
The AAT ontology
description
object
universe
instance of
1
1
description
dimension
class of
object type
object class
in dimension
1
value set
1
1
has
descriptor
descriptor
descriptor
value set
descriptor
1
value
has feature
value
class
constraint
23
Document fragment ontologies instructional
24
Domain ontology of a traffic light control system
25
Two ontologies of document fragments
26
Ontology for e-commerce
27
Top-level categoriesmany different proposals
Chandrasekaran et al. (1999)
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
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

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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
Inference abstract

• Operation input data set, output new given
• Example medical diagnosis temperature gt 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
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
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
Dealing with dynamic method selection
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