knowledge modelling: foundations, techniques and applications

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Knowledge Modelling: Knowledge Modelling: Foundations, Techniques and Foundations, Techniques and Applications Applications Enrico Motta Knowledge Media Institute The Open University United Kingdom

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Knowledge Modelling: Foundations, Techniques and Applications. Enrico Motta Knowledge Media Institute The Open University United Kingdom. Basic KBS Architecture. Inference Engine. User Interface. Domain Knowledge Base. First Generation KBS Architecture. Inference Engine. User - PowerPoint PPT Presentation

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Page 1: Knowledge Modelling: Foundations, Techniques and Applications

Knowledge Modelling:Knowledge Modelling:Foundations, Techniques and Foundations, Techniques and

ApplicationsApplications

Enrico MottaKnowledge Media Institute

The Open UniversityUnited Kingdom

Page 2: Knowledge Modelling: Foundations, Techniques and Applications

UserInterface

Domain Knowledge Base

InferenceEngine

Basic KBS Architecture Basic KBS Architecture

Page 3: Knowledge Modelling: Foundations, Techniques and Applications

UserInterface

Domain Knowledge Base

InferenceEngine

First Generation KBS First Generation KBS Architecture Architecture

Rule-basedBackward-chaining

Set of Domain rules

Page 4: Knowledge Modelling: Foundations, Techniques and Applications

UserInterface

Domain Knowledge Base

InferenceEngine

ProblemsProblems

Focus on implementation-level aspects (backward chaining) rather than knowledge-level functionalities (medical diagnosis)

Poor explanation capabilities

Difficult to assess competence

Low-level reuse support—Rules tend to be application specific

Page 5: Knowledge Modelling: Foundations, Techniques and Applications

Heuristic Classification ModelHeuristic Classification Model

Abstraction

Heuristic Match

Data

Refinement

Solutions

Clancey, AI Journal, 27, 1985

DataAbstractions

SolutionsAbstractions

Page 6: Knowledge Modelling: Foundations, Techniques and Applications

HC in Medical DiagnosisHC in Medical Diagnosis

Abstraction

Heuristic Match

Refinement

Solutions

DataAbstractions

SolutionsAbstractions

Low white blood count

Immunosuppressed

Data

Gram-negative Infection

E-coli Infection

Page 7: Knowledge Modelling: Foundations, Techniques and Applications

HC in Book SelectionHC in Book Selection

Abstraction

Heuristic Match

Refinement

Solutions

DataAbstractions

SolutionsAbstractions

Watches no TV

Educated Person Stereotype

Data

‘Intelligent Book’

Anna Karenina

Page 8: Knowledge Modelling: Foundations, Techniques and Applications

So What? (Competence vs So What? (Competence vs Performance)Performance)

Knowledge-level analysis shows what system actually does, not how it does it

—The interesting aspect about Mycin is its classification behaviour, not its depth-first control regime

—Separation of competence from performance (or specification from implementation)

» Important for both analysis and design of knowledge-intensive systems

Page 9: Knowledge Modelling: Foundations, Techniques and Applications

So What? (Levels of system So What? (Levels of system analysis)analysis)

There exist different levels at which a system can be described

—knowledge-level (tasks and problem solving methods)

—Symbol-level (backward-chaining)—Sub-symbol level (registers)

Shift in the level of analysis:—Wrong question: Can a problem be

solved by means of a rule-based system?—Right questions: What type of

knowledge-intensive task are we tackling? What are the appropriate problem solving methods?

Page 10: Knowledge Modelling: Foundations, Techniques and Applications

So What? (Reuse)So What? (Reuse)

Knowledge-level analysis uncovers generic reasoning patterns in problem solving agents

—E.g., heuristic classification

Shift from rule-based reuse to knowledge-level reuse

Focus on high-level reusable task models and reasoning patterns

—Classes of tasks » Design, diagnosis, classification, etc.

—Problem solving methods» Design methods, classification methods,

etc.

Page 11: Knowledge Modelling: Foundations, Techniques and Applications

So What? (Research & So What? (Research & Development)Development)

Model-based knowledge acquisition—From acquiring rules to instantiating task

models

Robust KBS development by reuse—KBS as a structured development process

» Robustness and economy—Importance of libraries—KBS development not necessarily an ‘art’!

Towards a practical theory of knowledge-based systems

—What are the classes of tasks/problem solving methods?

—How do we identify/model them? —When are methods appropriate?

Page 12: Knowledge Modelling: Foundations, Techniques and Applications

Knowledge-level ArchitecturesKnowledge-level Architecturesfor Sharing and Reusefor Sharing and Reuse

Application of the modelling paradigm to the specification and use of libraries of reusable components for knowledge

systems

Page 13: Knowledge Modelling: Foundations, Techniques and Applications

Modelling Frameworks (1)Modelling Frameworks (1)

A modelling framework identifies the generic types of knowledge which occur in knowledge systems, thus providing a generic epistemological organization for knowledge systems

Several exist —KADS/Common KADS - Un.of Amsterdam—Components of Expertise - Steels—Generic Tasks - Chandrasekaran—Role-limiting Methods - McDermott—Protégé - Musen, Stanford—TMDA - Motta—UPML - Fensel & Motta

Page 14: Knowledge Modelling: Foundations, Techniques and Applications

Modelling Frameworks (2)Modelling Frameworks (2)

Much in common—Emphasis on reusable models—Typology of generic tasks—Constructivist paradigm

Some differences—Different degrees of coupling between

domain-specific and domain-independent knowledge

—Different degrees of flexibility—Different typologies of knowledge

categories

Page 15: Knowledge Modelling: Foundations, Techniques and Applications

A Constructive Approach...A Constructive Approach...

Let’s define our own framework...

Page 16: Knowledge Modelling: Foundations, Techniques and Applications

Generic TasksGeneric Tasks

Informal definition—A generic class of applications - e.g.,

planning, design, diagnosis, scheduling, etc..

More precise definition—A knowledge-level, application-

independent description of the goal to be attained by a problem solver.

Several typologies exist—e.g., Breuker, 1994

Viewpoints over applications—No ‘natural categories’—Different viewpoints can be imposed on a

particular application

Page 17: Knowledge Modelling: Foundations, Techniques and Applications

Example: Parametric DesignExample: Parametric Design

Generic Task Parametric Design

Inputs: Parameters, Constraints, Requirements, Cost-Function, Preferences

Output: Design-Model

Goal: “To produce a complete and consistent design model,

which satisfies the given

requirements”

Preconditions: “At least one requirement and one parameter are provided”

Page 18: Knowledge Modelling: Foundations, Techniques and Applications

Example: ClassificationExample: Classification

Generic Task Classification Inputs: Candidate-classes

Observables

Output: Best-Matching-Classes

Preconditions: “At least one candidate

class exists”

Goal: “To find the class that best

explains the observables”

Page 19: Knowledge Modelling: Foundations, Techniques and Applications

Generic Component 2: Reusable Generic Component 2: Reusable PSMsPSMs

A domain-independent, knowledge-level specification of problem solving behaviour, which can be used to solve a class of tasks.

PSM specifications may be partial

PSM can be task-specific—E.g., heuristic classification

PSM can be task-independent—E.g., search methods, such as hill-

climbing, A*, etc.....

Page 20: Knowledge Modelling: Foundations, Techniques and Applications

Functional Specification of a PSMFunctional Specification of a PSM

Problem solving method search ontology import state-space-terminology competence roles input input: State output output: State preconditions input ≠ 0 postconditions solution_state (output) assumptions ?s . solution_state (?s) & successor

(input, ?s)

Page 21: Knowledge Modelling: Foundations, Techniques and Applications

Operational DescriptionOperational Description

Begin

states:= one x. initialize (input input)repeat

state:= one x . select _state (states states)if solution_state (state)then return state else succ_states:= one x. derive_successor_states (state

state) states:= one x. update_state_space (input1 states

input2 succ_states)end if

end repeat

end

Page 22: Knowledge Modelling: Foundations, Techniques and Applications

Task-Method StructuresTask-Method Structures

Problem Type

Primitive PSM

Page 23: Knowledge Modelling: Foundations, Techniques and Applications

Multi-Functional Domain ModelsMulti-Functional Domain Models

Domain-specific models, which are not committed to a specific PSM or task.

Examples—A database of cars—The CYC knowledge base, etc..

Page 24: Knowledge Modelling: Foundations, Techniques and Applications

Application Model

Picture so far..Picture so far..

Problem SolvingMethod

Classification Simple Classifier

Lunar rocks

Generic Task

Multi-Functional Domain

Page 25: Knowledge Modelling: Foundations, Techniques and Applications

Problem SolvingMethod

Classification Simple Classifier

Lunar rocks

Application Model

Generic Task

Multi-Functional Domain

IssueIssue

How to link different reusable components?

Page 26: Knowledge Modelling: Foundations, Techniques and Applications

Problem SolvingMethod

Classification

Task-DomainMapping

PSM-DomainMapping

Simple Classifier

Lunar rocks

Application Model

Generic Task

Multi-Functional Domain

Solution: MappingsSolution: Mappings

Mappings model explicitly the relationship between different components in an application model

Task-PSMMapping

Page 27: Knowledge Modelling: Foundations, Techniques and Applications

ExampleExample

Scenario: Office Allocation Application

Generic Task: Parametric Design

Domain: KB about employees and offices

Parameter

Employee

Design Model

Pairs<Employee, Room>

Task Level

Domain Level

Page 28: Knowledge Modelling: Foundations, Techniques and Applications

Mappings are an example of application-specific knowledge. Are there others?

Application-specific knowledgeApplication-specific knowledge

Yes: Application-specific heuristic problem solving knowledge

Page 29: Knowledge Modelling: Foundations, Techniques and Applications

Elevator Design ExampleElevator Design Example

A configuration designer only considers two positions for the counterweight

—Half way between platform and U-bracket—A position such that the distance between

the counterweight and the platform is at least 0.75 inches

Page 30: Knowledge Modelling: Foundations, Techniques and Applications

Complete PictureComplete Picture

Problem SolvingMethod

Generic Task

Multi-Functional Domain

MappingKnowledge

Application-specificProblem-Solving Knowledge

Application Configuration

Application Model

Page 31: Knowledge Modelling: Foundations, Techniques and Applications

Even More Complete PictureEven More Complete Picture

Problem SolvingMethod

Generic Task

Multi-Functional Domain

MappingKnowledge

Application-specificProblem-Solving Knowledge

Application Configuration

Domain Ontology

Task Ontology Method Ontology

Mapping Ontology Ontology

Application Model

Page 32: Knowledge Modelling: Foundations, Techniques and Applications