What is Knowledge? Prof. Elaine Ferneley [e-mail]

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What is Knowledge? Prof. Elaine Ferneley
E.Ferneley_at_salford.ac.uk
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Data, Information, and Knowledge

• Data Unorganized and unprocessed facts static
  a set of discrete facts about events
• Information Aggregation of data that makes
  decision making easier
• Knowledge is derived from information in the same
  way information is derived from data it is a
  persons range of information

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The DIKW Pyramid
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Some Examples

• Data represents a fact or statement of event
  without relation to other things.
• Ex It is raining.
• Information embodies the understanding of a
  relationship of some sort, possibly cause and
  effect.
• Ex The temperature dropped 15 degrees and then
  it started raining.
• Knowledge represents a pattern that connects and
  generally provides a high level of predictability
  as to what is described or what will happen next.
• Ex If the humidity is very high and the
  temperature drops substantially the atmospheres
  is often unlikely to be able to hold the moisture
  so it rains.
• Wisdom embodies more of an understanding of
  fundamental principles embodied within the
  knowledge that are essentially the basis for the
  knowledge being what it is. Wisdom is essentially
  systemic.
• Ex It rains because it rains. And this
  encompasses an understanding of all the
  interactions that happen between raining,
  evaporation, air currents, temperature gradients,
  changes, and raining.

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Definitions Data, Information, Knowledge,
Understanding and Wisdom

• Data is raw, it is a set of symbols, it has no
  meaning in itself
• Quantitatively measured by
• How much does it cost to capture and retrieve
• How quickly can it be entered and called up
• How much will the system hold
• Qualitatively measured by timeliness, relevance,
  clarity
• Can we access it when we need it
• Is it what we need
• Can we make sense of it
• In computing terms it can be structured as
  records of transactions usually stored in some
  sort of technology system

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Definitions Data, Information, Knowledge,
Understanding and Wisdom

• Information is data that is processed to be
  useful
• Provides answers to the who, what, where and when
  type questions
• given a meaning through a relational connector,
  often regarded as a message
• Sender and receiver
• Changes the way the receiver perceives something
  it informs them (data that makes a difference)
• Receiver decides if it is information (e.g. Memo
  perceived as information by sender but garbage by
  receiver)
• Information moves through hard and soft networks
• Transform data into information by adding value
  in various ways

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Definitions Data, Information, Knowledge,
Understanding and Wisdom

• Quantitative information management measures
  e.g.
• Connectivity (no. of email accounts, Lotus notes
  users)
• Transactions (no. of messages in a given period)
• Qualitative information management measures
• Informativeness (did I learn something new)
• Usefulness (did I benefit from the information)
• In computing terms a relational database makes
  information from the data stored within it

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Definitions Data, Information, Knowledge,
Understanding and Wisdom

• The application of data and information answers
  the how questions
• Collection of the appropriate information with
  the intent of making it useful
• By memorising information you amass knowledge
  e.g. memorising for an exam this is useful
  knowledge to pass the exam (e.g. 224)
• BUT the memorising itself does not allow you to
  infer new knowledge (e.g.1267342) to solve
  this multiplication requires cognitive and
  analytical ability the is achieved at the next
  level understanding
• In computing terms many applications (e.g.
  modelling and simulation software) exercise some
  type of stored knowledge

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Definitions Data, Information, Knowledge,
Understanding and Wisdom

• The appreciation of why
• The difference between learning and memorising
• If you understand you can take existing knowledge
  and creating new knowledge, build upon currently
  held information and knowledge and develop new
  information and knowledge
• In computing terms AI systems possess
  understanding in the sense that they are able to
  infer new information and knowledge from
  previously stored information and knowledge

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Definitions Data, Information, Knowledge,
Understanding and Wisdom

• Evaluated understanding
• Essence of philosophical probing
• Critically questions, particularly from a human
  perspective of morals and ethics
• discerning what is right or wrong, good or bad
• A mix of experience, values, contextual
  information, insight
• In computing terms may be unachievable can a
  computer have a soul??

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A Sequential Process of Knowing

• Understanding supports the transition from one
  stage to the next, it is not a separate level in
  its own right

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Rate of Motion towards Knowledge

• What is this (note the point when you realise
  what it is but do not say)
• I have a box.
• The box is 3' wide, 3' deep, and 6' high.
• The box is very heavy.
• When you move this box you usually find lots of
  dirt underneath it.
• Junk has a real habit of collecting on top of
  this box.
• The box has a door on the front of it.
• When you open the door the light comes on.
• You usually find the box in the kitchen.
• It is colder inside the box than it is outside.
• There is a smaller compartment inside the box
  with ice in it.
• When I open the box it has food in it.

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Rate of Motion towards Knowledge

• It was a refrigerator
• At some point in the sequence you connected with
  the pattern and understood
• When the pattern connected the information became
  knowledge to you
• If presented in a different order you would still
  have achieved knowledge but perhaps at a
  different rate

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From tacit to articulate knowledge

• We know more than we can tell.
• Michael Polanyi, 1966

High
Low
Codifiability
Tacit
Articulated
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We know more than we can tell.
Knowledge is experience, everything else is just
information. -Albert Einstein
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Explicit Knowledge

• Formal and systematic
• easily communicated shared in product
  specifications, scientific formula or as computer
  programs
• Management of explicit knowledge
• management of processes and information
• Are the activities to the right information or
  knowledge dependent ?

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Tacit Knowledge Examples

• Highly personal
• hard to formalise
• difficult (but not impossible)to articulate
• often in the form of know how.
• Management of tacit knowledge is the management
  of people
• how do you extract and disseminate tacit
  knowledge.

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Illustrations of the Different Types of Knowledge
Know that
Know how
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Knowledge As An Attribute of Expertise

• An expert in a specialized area masters the
  requisite knowledge
• The unique performance of a knowledgeable expert
  is clearly noticeable in decision-making quality
• Knowledgeable experts are more selective in the
  information they acquire
• Experts are beneficiaries of the knowledge that
  comes from experience

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Expertise, Experience Understanding

• Experience rules of thumb What e.g. gardener
  might have
• Understanding general knowledgeWhat a biology
  graduate might have
• Expertise E U in harmonyWhat an expert has

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Expertise, Experience Understanding 2
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ReasoningandThinkingandGenerating Knowledge
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Experts Reasoning Methods

• Reasoning by analogy relating one concept to
  another
• Formal reasoning using deductive or inductive
  methods (see next slide)
• Case-based reasoning reasoning from relevant
  past cases

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Deductive and inductive reasoning

• Deductive reasoning exact reasoning. It deals
  with exact facts and exact conclusions
• Inductive reasoning reasoning from a set of
  facts or individual cases to a general conclusion

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Types (Categorization) of Knowledge

• Shallow (readily recalled) and deep (acquired
  through years of experience)
• Explicit (already codified) and tacit (embedded
  in the mind)
• Procedural (repetitive, stepwise) versus
  Episodical (grouped by episodes)
• Knowledge exist in chunks