Knowledge representation
Sponsored Links
This presentation is the property of its rightful owner.
1 / 31

Knowledge Representation PowerPoint PPT Presentation

  • Uploaded on
  • Presentation posted in: General

Knowledge Representation. Representational adequacy declarative, procedural Inferential adequacy manipulate knowledge incorporate new knowledge. Types of Knowledge. Simple facts Complex organized knowledge procedure - how to knowledge meta-knowledge. Semantic Data Models.

Download Presentation

Knowledge Representation

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

Knowledge representation

Knowledge Representation

  • Representational adequacy

    • declarative, procedural

  • Inferential adequacy

    • manipulate knowledge

    • incorporate new knowledge

Types of knowledge

Types of Knowledge

  • Simple facts

  • Complex organized knowledge

  • procedure - how to knowledge

  • meta-knowledge

Semantic data models

Semantic Data Models

  • High level model of model of conceptual model

  • Not tied to implementation concerns

  • Focus on

    • expressiveness

    • simplicity

    • concise

    • formality

Semantic nets

Semantic Nets

  • Nodes represent Objects

  • Links or Arcs represent Relationships

    • “instance of” - set membership

    • “is a” - inheritance

    • “ has a” - attribute descriptors

    • “part of” - aggregation

Knowledge representation

Is a

Has a


Instance of

Semantic nets advantages disadvantages


easy to understand

support inheritance

“natural” way to represent knowledge

Hard to deal with exceptions

procedural knowledge difficult to represent

no standards for defining nodes or relationships

Semantic NetsAdvantages Disadvantages

Classes objects attributes values object orientation

Classes, Objects, Attributes, Values - Object Orientation

  • Classes describe common properties of objects

  • Objects may be physical or conceptual

  • Attributes are characteristics of objects

  • Values are specific measures of Attributes for specific instances



  • Specify common properties of instances

  • support hierarchical classification

  • superclass / subclass

    • subclass may be more refined version

    • each subclass inherits operations and attributes of its ancestors

    • subclass may have its own operations and attributes

Objects or instances

Objects or Instances

  • Refers to things identified in model of conceptual model

    • may be tangible (equipment, part, orders, squashed bananas)

    • may be mental constructs

Class vs instances

Class vs instances

Person class




  • Sharing attributes and behaviors within a class of objects







Sale Manager



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


size, location,





  • Each object responds in its unique way to messages

When changed method

When needed method

Object orientation


  • Tool for managing complexity

  • emphasis on object structure

  • specify “what is”

  • mapped directly from semantic net

Rule representations

Rule Representations

  • Rules are called productions

  • Rule have two parts

    • condition part, premise -> IF

    • action part ,conclusion-> THEN

  • The action can add a fact to the knowledge base, start a procedure or display a screen

Rules represent knowledge

Rules represent knowledge

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

  • IF air vehicle is a plane AND plane maximum altitude is 40000 AND plane manufacturer is Boeing THEN ASK Flight Display 15

Representing knowledge

Representing knowledge

  • Abstracting with rules

    • translate quantitative to qualitative

    • define technical terms

    • support generalized reasoning

  • make rules for user

    • easy to understand

    • help user follow decision logic

Rule for understanding

Rule for understanding

  • Quantitative to Qualitative

    • qualitative language is easier to understand

    • interpretation of numerical data

    • make user feel comfortable with decision logic

  • If temperature > 200 and humidity is 85% then machine is slightly overheated

Definitional rules

Definitional Rules

  • Help communicate and train users

  • Help user understand vocabulary

  • Promotes common agreement on terms for expert, user and knowledge engineer

  • IF you want more than one source file of classes THEN use package keyword

Rules support generalizations

Rules support Generalizations

  • Allow reasoning with from specialization to generalizations

  • Support classification of objects at higher levels

  • Support refinements

Knowledge representation

Surface Knowledge

  • Hard to understand

  • Difficult to learn reasoning strategies

  • hard to update and expand knowledge base

If pump operation temperature is over 300

AND water mixture pH > 5.2

THEN replace pump bearing and oil

Hierarchical classification

Hierarchical Classification

Abstraction draws out important aspects

Solution abstractions

Feature abstractions

Heuristic Match





Deep knowledge

Deep knowledge

Lubrication defect

Is a

Poor Oil Viscosity



Hot Pump

Low Temp

temperature is over 300

water mixture pH > 5.2

Reasoning at higher level

Reasoning at higher level


Lubrication defect


Type of

Fix heat



Replace bearing

and oil

Rules advantages disadvantages

Modular style - easy to add, update and delete

natural for many problem domains

uncertain knowledge may be represented

May be difficult to understand

may demonstrate unpredictable behavior

extra effort required to representing structural knowledge

Rules Advantages Disadvantages

Predicate logic

Predicate Logic

  • Programming by description

  • describe the problem’s facts

  • built in inference engine combines and uses facts and rules to make inferences

Prolog programming

Prolog Programming

  • Declaring facts about objects and their relationships -> likes (john,mary)

  • Defining rules about objects and relationships

  • Asking Questions about objects

sister-of(X,Y) :- female(X),





  • Similar to objects

  • helps organize entities

  • packages operations (demons)

  • easy to modify

  • extensible through inheritance

Mammal frame

Mammal Frame

Frame natural representation

Frame - natural representation

  • Can accommodate a taxonomy of knowledge

  • contains defaults expectations

  • represent procedural and declarative knowledge

Facets properties of slots

Facets - properties of slots

  • Login