What is Knowledge? What is Knowledge?
Prof. Elaine Ferneley Prof. Elaine Ferneley [email protected]@salford.ac.uk
Prof Elaine Ferneley
Data, Information, and KnowledgeData, 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 person’s range of information
Prof Elaine Ferneley
Some ExamplesSome 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.
Prof Elaine Ferneley
KNOWLEDGE
INFORMATION
WISDOM
Nonalgorithmic(Heuristic)
Nonprogrammable
From Data Processing to Knowledge-based SystemsFrom Data Processing to Knowledge-based Systems
DATAAlgorithmic Programmable
The DIKW PyramidThe DIKW Pyramid
Prof Elaine Ferneley
Definitions:Definitions: DataData, , Information, Knowledge, Information, Knowledge, Understanding and WisdomUnderstanding 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
Prof Elaine Ferneley
Definitions: Definitions: DataData, , InformationInformation, Knowledge, , Knowledge, Understanding and WisdomUnderstanding 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
Prof Elaine Ferneley
Definitions: Definitions: DataData, , InformationInformation, Knowledge, , Knowledge, Understanding and WisdomUnderstanding 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
Prof Elaine Ferneley
Definitions: Definitions: DataData, , Information, Information, KnowledgeKnowledge, , Understanding and WisdomUnderstanding 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. 2*2=4)
BUT the memorising itself does not allow you to infer new knowledge (e.g.1267*342) – 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
Prof Elaine Ferneley
Definitions: Definitions: DataData, , Information, Knowledge, Information, Knowledge,
UnderstandingUnderstanding and Wisdom 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
Prof Elaine Ferneley
Definitions: Definitions: DataData, , Information, Knowledge, Information, Knowledge,
Understanding and Understanding and WisdomWisdom
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??
Prof Elaine Ferneley
A Sequential Process of KnowingA Sequential Process of Knowing
Understanding supports the transition from one stage to the next, it is not a separate level in its own right
Prof Elaine Ferneley
Rate of Motion towards KnowledgeRate 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.
Prof Elaine Ferneley
Rate of Motion towards KnowledgeRate 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
Prof Elaine Ferneley
LearningLearning
Learning by experience: a function of time and talent
Learning by example: more efficient than learning by experience
Learning by sharing, education.
Learning by discovery: explore a problem area.
Prof Elaine Ferneley 15
From tacit to articulate knowledge From tacit to articulate knowledge
““We know more than we can tell.” We know more than we can tell.”
Michael Polanyi, 1966Michael Polanyi, 1966
TacitArticulated
High Low
MANUALHow to
play soccer
Codifiability
Prof Elaine Ferneley 1616
Knowledge is experience, Knowledge is experience, everything else is just everything else is just
information.information.-Albert Einstein-Albert Einstein
““We know more than we can tell.”We know more than we can tell.”
Prof Elaine Ferneley
Explicit KnowledgeExplicit Knowledge
Mend a
broken legCalculate
tax
Make a cake
Raise a
n
invoiceBuild anengine
Service a boiler
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 ?
Prof Elaine Ferneley
Tacit Knowledge ExamplesTacit Knowledge Examples
Work in
team
Get 100%in an
assignmentCo-ordinate colours
Ride a
bikeDesign apresentation
Arrange furniture
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.
Prof Elaine Ferneley
Illustrations of the Different Types of Illustrations of the Different Types of Knowledge Knowledge
Know ‘that’
Know ‘how’
Prof Elaine Ferneley
Knowledge As An Attribute of ExpertiseKnowledge 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
Prof Elaine Ferneley
Expertise, Experience & UnderstandingExpertise, Experience & Understanding
Experience – rules of thumb: What e.g. gardener might have
Understanding – general knowledge:What a biology graduate might have
Expertise – E + U in harmonyWhat an expert has
Prof Elaine Ferneley
Expertise, Experience & Understanding 2Expertise, Experience & Understanding 2
Prof Elaine Ferneley
ReasoningReasoningandand
ThinkingThinkingandand
Generating KnowledgeGenerating Knowledge
Prof Elaine Ferneley
Expert’s Reasoning MethodsExpert’s 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
Prof Elaine Ferneley
Deductive and inductive reasoningDeductive and inductive reasoning
Deductive reasoning: exact reasoning. It deals with exact facts and exact facts and exact conclusionsexact conclusions
Inductive reasoning: reasoning from a set of facts or individual cases to a general general conclusionconclusion