001.intro to knowledge representation

8/11/2019 001.Intro to Knowledge Representation

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Knowledge Representation

Representational adequacydeclarative, procedural

Inferential adequacymanipulate knowledgeincorporate new knowledge


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Types of Knowledge

Simple factsComplex organized knowledgeprocedure - how to knowledgemeta-knowledge


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Semantic Data Models

High level model of model of conceptualmodelNot tied to implementation concernsFocus on

expressivenesssimplicityconciseformality


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Semantic Nets

Nodes represent ObjectsLinks or Arcs represent Relationships

instance of - set membership is a - inheritance has a - attribute descriptors part of - aggregation


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Has a

Part-of

Instance of

Is a


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Semantic Nets

Advantages DisadvantagesFlexibleeasy to understand

support inheritance natural way torepresent knowledge

Hard to deal withexceptions

procedural knowledgedifficult to representno standards fordefining nodes orrelationships


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Classes, Objects, Attributes,Values - Object Orientation

Classes describe common properties ofobjects

Objects may be physical or conceptual Attributes are characteristics of objects Values are specific measures of Attributesfor specific instances


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Classes

Specify common properties of instancessupport hierarchical classificationsuperclass / subclass

subclass may be more refined versioneach subclass inherits operations and

attributes of its ancestorssubclass may have its own operations and

attributes


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Objects or Instances

Refers to things identified in modelof conceptual model

may be tangible (equipment, part,orders, squashed bananas)may be mental constructs


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Class vs instances

instances


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Inheritance

Sharingattributes and

behaviorswithin a class ofobjects

Person

customer

Employee

SalesPerson Manager

Sale Manager


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Encapsulation

Attributes and behaviors (methods)integrated with the classes and objects

Attributes:size, location,

appearance


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Polymorphism

Each object responds in its unique way tomessages When changed method

When needed method


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Object-Orientation

Tool for managing complexityemphasis on object structurespecify what is mapped directly from semantic net


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Rule Representations

Rules are called productionsRule have two parts

condition part, premise -> IFaction part ,conclusion-> THEN

The action can add a fact to theknowledge base, start a procedure ordisplay a screen


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Rules represent knowledge

Apply O-A-V framework (object-attribute-value)

IF air vehicle is a plane AND planemaximum altitude is 40000 AND planemanufacturer is Boeing THEN ASK Flight

Display 15


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Representing knowledge

Abstracting with rulestranslate quantitative to qualitative

define technical termssupport generalized reasoning

make rules for user

easy to understandhelp user follow decision logic


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Rule for understanding

Quantitative to Qualitativequalitative language is easier to understand

interpretation of numerical datamake user feel comfortable with decision

logic

If temperature > 200 and humidity is85% then machine is slightly overheated


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Definitional Rules

Help communicate and train usersHelp user understand vocabularyPromotes common agreement on termsfor expert, user and knowledge engineerIF you want more than one source file ofclasses THEN use package keyword


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Rules supportGeneralizations

Allow reasoning with from specializationto generalizations

Support classification of objects at higherlevelsSupport refinements


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If pump operation temperature is over 300AND water mixture pH > 5.2THEN replace pump bearing and oil

Surface Knowledge

Hard to understand Difficult to learnreasoning strategies hard to update andexpand knowledge base


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Hierarchical Classification

Feature abstractions Solution abstractions

Features Recommendations

generalize

Heuristic Match

refine

Abstraction draws out important aspects


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Deep knowledge

Hot Pump Low Temp

Poor Oil Viscosity

Lubrication defect

causescauses

Is a

water mixture pH > 5.2temperature is over 300


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Reasoning at higher level

Lubrication defectrequires

Maintenance

Fix heatdamage

Replace bearingand oil

Type of

Remedy


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Modular style - easyto add, update anddeletenatural for manyproblem domainsuncertain knowledgemay be represented

May be difficult tounderstand

may demonstrateunpredictablebehaviorextra effort requiredto representingstructural knowledge

RulesAdvantages Disadvantages


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Programming by descriptiondescribe the problems facts

built in inference engine combines anduses facts and rules to make inferences

Predicate Logic


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Mammal Frame

Slot Values Default Demons

Skin Fur

Birth Live

Legs 4

Weight Computedemon


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Frame - naturalrepresentation

Can accommodate a taxonomy ofknowledge

contains defaults expectationsrepresent procedural and declarativeknowledge


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Facets Inference Value Prompt

Exhaustive Conf

SearchOrder

Default Expand

When

Changed

Init Query

FromWhenNeeded

Reinit Unknown

Facets - properties of slots

001.intro to knowledge representation

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