A One-Page Summary Of The Article

A One-Page Summary Of The Article

Knowledge in Design

Kyoung-yun “Joseph” Kim/Wayne State University

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What is Knowledge?

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Data, information & knowledge (1/2)  The Old Pyramid  data

 information  knowledge

 wisdom

 Information that changes something or somebody—becoming grounds for action by making an individual, or institution capable of different, more effective action

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Data, information & knowledge (2/2)  Data  “raw signals”

. . . – – – . . .

 Information  meaning attached to data


 Knowledge  attach purpose and competence to information  potential to generate action

emergency alert → start rescue operation

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Our First Question: What Is Knowledge?  Putting the question this way makes the

question sound really hard. Here are two other ways to put it:  “What is it to know something?”  “Under what conditions is it true that a person

qualifies as knowing that something is the case?”

 An answer to this question will be a theory of knowledge.

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What is a theory of knowledge? A theory of knowledge is a statement of the conditions under which a person knows that something is the case.

It is a statement of this form:


S knows that p if and only if Sp .

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Further Clarification of the Question ‘What is Knowledge?’ Three Ways the Word ‘Knows’ Is Used:  “Bob knows how to ride a bicycle.”  “Bob knows the president of the U.S.”  “Bob knows that the earth is round.”


The theories of knowledge we’re looking at are about the third kind of knowledge – called knowledge that, or propositional knowledge.

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Further Attributes of Knowledge  Know-how  Know-why  Know-what  Know-who  Know-where  Know-when

(Collison and Parcell, 2001)

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Some Associated Notions  Knowledge can be defined as the “understanding obtained through the

process of experience or appropriate study.”’  Knowledge can also be an accumulation of facts, procedural rules, or

heuristics.  A fact is generally a statement representing truth about a subject matter or domain.  A procedural rule is a rule that describes a sequence of actions.  A heuristic is a rule of thumb based on years of experience.

 Intelligence implies the capability to acquire and apply appropriate knowledge.

 Memory indicates the ability to store and retrieve relevant experience according to will.

 Learning represents the skill of acquiring knowledge using the method of instruction/study.

 Experience relates to the understanding that we develop through our past actions.  Knowledge can develop over time through successful experience, and experience can lead

to expertise.

 Common sense refers to the natural and mostly unreflective opinions of humans.

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Cognitive Psychology (1/2)  Cognitive psychology tries to identify the cognitive

structures and processes that closely relates to skilled performance within an area of operation.  It provides a strong background for understanding knowledge and expertise.  In general, it is the interdisciplinary study of human intelligence.

 The two major components of cognitive psychology are:  Experimental Psychology: This studies the cognitive processes that

constitutes human intelligence.  Artificial Intelligence (AI): This studies the cognition of Computer-based

intelligent systems.

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Cognitive Psychology (2/2)  The process of eliciting and representing experts knowledge

usually involves a knowledge developer and some human experts (domain experts).

 In order to gather the knowledge from human experts, the developer usually interviews the experts and asks for information regarding a specific area of expertise.

 Almost impossible for humans to provide the completely accurate reports of their mental processes.

 Cognitive psychology: helps to a better understanding of what constitutes knowledge, how knowledge is elicited, and how it should be represented in a corporate knowledge base.

 Contributes a great deal to the area of knowledge management.

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Kinds of Knowledge (1/4)  Deep Knowledge: Knowledge acquired through years of

proper experience.  Shallow Knowledge: Minimal understanding of the problem

area.  Knowledge as Know-How: Accumulated lessons of practical


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Kinds of Knowledge (2/4)  Reasoning and Heuristics: Some of the ways in which humans reason are as

follows:  Reasoning by analogy: relating one concept to another.  Formal Reasoning: reasoning by using deductive (exact) or inductive reasoning.

 Deduction uses major and minor premises.  In case of deductive reasoning, new knowledge is generated by using previously specified

knowledge.  Inductive reasoning implies reasoning from a set of facts to a general conclusion.  Inductive reasoning is the basis of scientific discovery.  A case is knowledge associated with an operational level.

 Common Sense: This implies a type of knowledge that almost every human being possess in varying forms/amounts.

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Kinds of Knowledge (3/4)  Knowledge on the basis of whether it is procedural, declarative,

semantic, or episodic.  Procedural knowledge represents the understanding of how to carry out

a specific procedure.  Declarative knowledge is routine knowledge about which the expert is

conscious. It is shallow knowledge that can be readily recalled since it consists of simple and uncomplicated information. This type of knowledge often resides in short-term memory.

 Semantic knowledge is highly organized, “chunked” knowledge that resides mainly in long-term memory. Semantic knowledge can include major concepts, vocabulary, facts, and relationships.

 Episodic knowledge represents the knowledge based on episodes (experimental information). Each episode is usually “chunked” in long- term memory.

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Kinds of Knowledge (4/4)  tacit or explicit  Tacit knowledge usually gets embedded in human mind

through experience.  Explicit knowledge is that which is codified and digitized in

documents, books, reports, spreadsheets, memos etc.

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Expert Knowledge  It is the information woven inside the mind of an

expert for accurately and quickly solving complex problems.

 Knowledge Chunking  Knowledge is usually stored in experts long-range memory

as chunks.  Knowledge chunking helps experts to optimize their

memory capacity and enables them to process the information quickly.

 Chunks are groups of ideas that are stored and recalled together as an unit.

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Expert vs. nonexpert  Expert

 In order to become an expert in a particular area, one is expected to master the necessary knowledge and make significant contributions to the concerned field.

 The unique performance of a true expert can be easily noticed in the quality of decision making.

 The true experts (knowledgeable) are usually found to be more selective about the information they acquire, and also they are better able in acquiring information in a less structured situation.

 They can quantify soft information, and can categorize problems on the basis of solution procedures that are embedded in the experts long range memory and readily available on recall.

 Hence, they tend to use knowledge-based decision strategies starting with known quantities to deduce unknowns.

 If a first-cut solution path fails, then the expert can trace back a few steps and then proceed again.

 Nonexpert  Use means-end decision strategies to approach the problem scenario.  Usually focus on goals rather than focusing on essential features of the task which makes

the task more time consuming and sometimes unreliable.  Specific individuals are found to consistently perform at higher levels than others and they

are labeled as experts.

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Knowledge Power  Knowledge is power!  Well-accepted for individuals  Now pursued by organizations  “Knowledge management”

 Broad enterprise interest  Knowledge as critical corporate asset  Knowledge superiority in Network Centric


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Knowledge Uniqueness  Knowledge is unique  Not data (facts, bits)  Not information (messages, context)  Enables action (good decisions, behaviors)


Knowledge (understanding)

Information (processed data, knowledge)

Data (raw data)

What about – Omniscience? – Enlightenment? – Wisdom?



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Knowledge Flow?

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Knowledge Flow  Knowledge flow is important  Knowledge not evenly distributed  Flow across time, space, organizations

 Knowledge flow not well understood  Electrical flow  amplifier, IC  Air flow  wing, engine  Knowledge flow  K amplifier, K engine (?)

 Not the stuff sent across networks/comms

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Kn ow

le dg

e In fo

rm at

io n D

at a

? Hierarchical Layered Flow

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Knowledge engineering  Process of  eliciting,  structuring,  formalizing,  operationalizing

 Information and knowledge involved in a knowledge- intensive problem domain,

 In order to construct a program that can perform a difficult task adequately

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Problems in knowledge engineering  Complex information and knowledge is difficult to

observe  Experts and other sources differ  Multiple representations:  textbooks  graphical representations  heuristics  skills

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Importance of proper knowledge engineering  Knowledge is valuable and often outlives a particular

implementation  knowledge management

 Errors in a knowledge-base can cause serious problems

 Heavy demands on extendibility and maintenance  Changes over time

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Knowledge Explosion



Information Companies



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What is knowledge management (KM)  Knowledge management can be difficult to define, because it

encompasses a wide range of practices, tools, concepts, and techniques

 KM is the process through which organizations generate value from their intellectual and knowledge-based assets

 Most often, generating value from such assets involves codifying what employees, partners and customers know, and sharing that information among employees, departments and even with other companies in an effort to devise best practices

 It’s important to note that the definition says nothing about technology; while KM is often facilitated by IT, technology by itself is not KM.

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Knowledge Management (KM)  An integrated systematic approach to identifying,

managing and sharing all of an enterprise’s information assets, including databases, documents, policies, and procedures, as well as previously unarticulated expertise and experience held by individual workers.

 Fundamentally it is about making the collective information and experience of an enterprise available to individual worker.

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Why is knowledge management important?  Knowledge is often an organisations most valuable asset  Aging populations in many countries means imminent

mass retirements how is the knowledge of these employees going to be captured

 Outsourcing transfer of knowledge from parent company to vendor

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KM Concepts  Knowledge as enterprise asset  Hiring for experience over intelligence  Sustainable competitive advantage  Firm: “org that knows how to do things”

 Unique economics of knowledge  Create without cost  Share without losing  Increasing marginal returns to scale  Very human endeavor

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Components of KM Programs  People – communities and networks  Processes – knowledge-enabled  Technology – collaboration, knowledge leverage tools  Content (Culture) – best practices, internal and

external intelligence

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Activities of Managing Knowledge  Create  Discover  Capture  Distil  Validate  Share  Adapt  Adopt  Transfer  Apply

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Knowledge Management Approaches  Self-service – intranet portals; yellow pages; people

finder  Networks and Community of Practice – knowledge

sharing; learning communities  Facilitated transfer – internal consultants; dedicated

facilitators; known experts

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Sustainable Knowledge Management  Unconscious incompetence  Conscious incompetence  Conscious competence  Unconscious competence


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Intellectual Assets  Social capital – relationships with customers, employees,

business partners and external experts  Structural capital – patents; brand names; systems and

processes; management philosophy  Human capital – education; experience; skills; attitudes

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Organisational vs Individual Knowledge  Two issues:  Corporate knowledge owned by individuals  Knowledge resides in silos

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Current PD Knowledge Explosion  PD knowledge is exploding [Stevens, OECDO, 1996]  PD Knowledge is not managed in order to reuse appropriate

PD process [Arkell, BF, 2007, Ruggles, CMR, 1998]  Even though the knowledge exists, often not available/accessible

[DeLong, OUP, 2004, Ruggles, CMR, 1998]

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Information Companies



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Knowledge Loss among PD Processes

Planning Conceptual Design Detailed Design Prototyping Test

PD Processes

Missing Knowledge

Total Knowledge


Knowledge Accumulation

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Product Development Knowledge







Co nc

ep t d

es ig


De ta

il d es

ig n

Pr ot

ot yp

in g

Ma nu

fa ct

ur in


Customers Designers



PD knowledge …

Knowledge Management System Technology Store & Retrieve

Send Structure & Navigate

Share Synthesize Solve

M aturity

KMS requirments



Linguistic Search

Query Tool

Data Warehousing


Document Management






Electronic Meeting Support

Video Conferencing

Discussion DB

Content Extraction


Rule-based Reasoning

Neural Network

Data Mining

(Adapted from Gatner group)40


DCR and knowledge evaluation

Casual knowledge network & transformation

Casual reasoning

Web-based collaboration

Casual knowledge integration


FCM Constructor

CInDI Lab.

Knowledge in Design
What is Knowledge?
Data, information & knowledge (1/2)
Data, information & knowledge (2/2)
Our First Question: �What Is Knowledge?
What is a theory of knowledge?
Further Clarification of the Question ‘What is Knowledge?’
Further Attributes of Knowledge
Some Associated Notions
Cognitive Psychology (1/2)
Cognitive Psychology (2/2)
Kinds of Knowledge (1/4)
Kinds of Knowledge (2/4)
Kinds of Knowledge (3/4)
Kinds of Knowledge (4/4)
Expert Knowledge
Expert vs. nonexpert
Knowledge Power
Knowledge Uniqueness
Knowledge Flow?
Knowledge Flow
Slide Number 22
Knowledge engineering
Problems in knowledge engineering
Importance of proper knowledge engineering
Knowledge Explosion
What is knowledge management (KM)
Knowledge Management (KM)
Why is knowledge management important?
KM Concepts
Components of KM Programs
Activities of Managing Knowledge
Knowledge Management Approaches
Sustainable Knowledge Management
Intellectual Assets
Organisational vs Individual K

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