advanced knowledge modelling
DESCRIPTION
Ch. 13 of the CommonKADS textbookTRANSCRIPT
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")
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Two different organizations of the disease hierarchy
infec tion
mening itis pneumonia
bacteria lpneumonia
acute v ira lpneumonia
chronic v ira lpneumonia
v ira lpneumonia
infec tion
mening itis pneumonia
chronicpneumonia
acute v ira lpneumonia
acute bac teria lpneumonia
acutepneumonia
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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;
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Viewpoint: graphical representation
infec tion
acuteinfec tion
chronicinfec tion
viralinfec tion
bac terialinfec tionmening itispneumonia
acute viralmening itis
c ausal agenttime fac tor
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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
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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
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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;
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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;
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Constraint rules
■ Rules about restrictions on a single concept ■ No antecedent or consequent
component
componentcons tra int
R ULE -‐T Y P E component-‐cons tra int; CONS T R AINT : component;E ND R ULE -‐T Y P E component-‐cons tra int;
E xample cons tra ints (ca r is a component):
ca r.we ight < 500 kgca r.leng th < 5.5 m
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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”
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
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
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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
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
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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
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Ontology specification
■ Many different languages ➤ KIF ➤ Ontolingua ➤ Express ➤ LOOM ➤ UML ➤ ......
■ Common basis ➤ Class (concept) ➤ Subclass with inheritance ➤ Relation (slot)
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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
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
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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
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Art & Architecture Thesaurus
Used for indexing stolen art objects in European police databases
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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+
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Document fragment ontologies: instructional
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Domain ontology of a traffic light control system
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Two ontologies of document fragments
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Ontology for e-commerce
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Top-level categories: many different proposals
Chandrasekaran et al. (1999)
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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
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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
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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.
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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.
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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
Advanced knowledge modelling 34
Dealing with dynamic method selection
a s s oc ia tivegenera tion
genera tehypothes is
mode l-‐ba s edgenera tion
genera tions tra teg y
heuris ticma tch
caus a lcovering
genera tehypothes is
caus a lcovering
s ingle methodfor hypothes isgeneration
work-‐around for multiple methods for the same task
obta inna ture of da ta
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