Supervised by, Mr. Ashraf Yaseen

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Supervised by,Mr. Ashraf Yaseen
Knowledge Acquisitioning
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Overview.

• Brief Introduction about Knowledge Acquisition.
• How it can be achieved?.
• KA Stages.
• Model.
• Problems that are encountered
• KADS.
• KADS Principles.
• References.

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Brief Introduction

• Definition
• Knowledge Acquisition (KA) is the transfer and
  transformation of potential problem solving
  expertise from some knowledge source to a
  program.
• Knowledge Acquisition is a large process itself
  and is composed of five stages which will be
  explained later.

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How it can be achieved???

• Could be achieved by a computer program that
  creates associations using a large body of case
  data.
• Knowledge Elicitation Series of interviews
  between the domain expert and the knowledge
  engineer who then writes a computer program
  representing the knowledge.
• Interaction between a domain expert and a
  computer program

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KA Stages

• The five stages in the KNOWLEDGE ACQUISITION
  Process
• 1) Identification- Identifies the problem
  characteristics
• 2) Conceptualization - Finds Concepts to
  represent the knowledge
• 3) Formalization - Designs the structure to
  organize the knowledge
• 4) Implementation - Formulates rules, frames etc.
  to represent the knowledge
• 5) Testing - Validates the rules that organize
  the knowledge

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KA Stages
Reformulation
Redesign
Refinements
Identification
Conceptualization
Testing
Formalization
Implementation
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Model

• After the knowledge engineer gets the Information
  from the expert, this data is used to construct
  an appropriate model.
• KADS is an example of a model contains four
  layers (Strategy, Task, Inference, Domain).
• Other examples are task based conceptual model
  like (OPAL system), Metalevel acquisition tool
  like (DOTS system), and other models..

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Problems..

• There may be some problems with transferring
  knowledge in the process of knowledge acquisition
  like
• Experts expressing his/her knowledge. (the expert
  uses a process to solve problems this process
  is internal)
• 2) Transferring the Knowledge to a machine. (in
  an organized way, In order for the machine to
  understand or make any sense of the knowledge it
  has to be in a more detailed/lower level than a
  human would use).
• 3) The number of participants involved in the
  transfer. (may cause problems between
  participants.)
• 4) Extracting not only the knowledge but its
  actual structure too.

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KADS

• KADS is framework for a modeling approach of
  knowledge engineering.
• Knowledge-Based systems are not just containers
  of knowledge
• They are operational model that exhibits some
  desired behavior and impacts real-world phenomena
• Knowledge elicitation is not just eliciting
  domain knowledge but also interpreting this data
  with respect of some conceptual framework, and
  formalize it in such a way that the program can
  actually use the knowledge

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KADS Principles

• Introduction of multiple models to cope with
  knowledge engineering complexity
• The KADS four-layer framework for modeling the
  required expertise.
• The reusability of generic model components.
• The process of differentiating simple models into
  more complex ones.
• The importance of structure-perceiving
  transformation of models of expertise into design
  and implementation.

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Continue...

• The motivation of KADS is to manage complexity.
• The first principle provides multiple models to
  help knowledge engineering in facing some issues
• Defining the problem that the expert system is
  meant to solve.
• Defining the function of the expert system
• Defining the tasks that must be performed to
  fulfill the expert systems function

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References

• Introduction to Expert System, 3rd edition,
  Peter Jackson.
• http//www.epistemics.co.uk/Notes/63-0-0.htm
• http//www.scism.sbu.ac.uk/inmandw/review/knowacq/
  review/rev11656.html

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Thank You For
Listening