Advanced knowledge modelling

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

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")

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

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;

Viewpoint:
graphical representation
infection
acute
infection
chronic
infection
viral
infection
bacterial
infection
mening itispneumonia
acute
viral
mening itis
causal
agent
time
factor

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

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

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;

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;

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

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”

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

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

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

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

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

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

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

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

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

Art & Architecture Thesaurus
Used for
indexing
stolen art
objects in
European
police
databases

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+

Document fragment ontologies:
instructional

Domain ontology of a traffic
light control system

Two ontologies of document
fragments

Ontology for e-commerce

Top-level categories:
many different proposals
Chandrasekaran et al. (1999)

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

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

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

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.

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.

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

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

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

Advanced knowledge modelling

More Related Content

Viewers also liked (14)

Similar to Advanced knowledge modelling (20)

More from Guus Schreiber (20)

Recently uploaded (20)

Advanced knowledge modelling