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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http://www.solers.com/illustrations/pyramid.gif
http://images.google.com/imgres?imgurl=users.rcn.com/dway/cindblk.shelt..gif&imgrefurl=http://users.rcn.com/dway/fall.concrete.html&h=209&w=315&prev=/images?q=Individual+Action&svnum=10&hl=en&lr=&ie=UTF-8&oe=UTF-8&sa=G
Data, information & knowledge (2/2) Data “raw signals”
. . . – – – . . .
Information meaning attached to data
S O S
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:
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S knows that p if and only if Sp .
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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.”
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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
experience.
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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
Warfare
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Knowledge Uniqueness Knowledge is unique Not data (facts, bits) Not information (messages, context) Enables action (good decisions, behaviors)
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Knowledge (understanding)
Information (processed data, knowledge)
Data (raw data)
What about – Omniscience? – Enlightenment? – Wisdom?
Actionability
Abundance
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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
Books
Experts
Information Companies
Communities
Internets
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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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http://en.wikipedia.org/wiki/Four_stages_of_competence
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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Books
Experts
Information Companies
Communities
Internets
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Knowledge Loss among PD Processes
Planning Conceptual Design Detailed Design Prototyping Test
PD Processes
Missing Knowledge
Total Knowledge
Gap
Knowledge Accumulation
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Product Development Knowledge
Products
Processes
Cup
Pen
Radio
Car
Co nc
ep t d
es ig
n
De ta
il d es
ig n
Pr ot
ot yp
in g
Ma nu
fa ct
ur in
g
Customers Designers
Systems
Actors
PD knowledge …
…
…
Knowledge Management System Technology Store & Retrieve
Send Structure & Navigate
Share Synthesize Solve
M aturity
KMS requirments
High
Low
Linguistic Search
Query Tool
Data Warehousing
DBMS
Document Management
Internet/Intranet
Netcasting
WWW/ HTML
Workflow
Electronic Meeting Support
Video Conferencing
Discussion DB
Content Extraction
Agents
Rule-based Reasoning
Neural Network
Data Mining
(Adapted from Gatner group)40
OWL/SWRL
DCR and knowledge evaluation
Casual knowledge network & transformation
Casual reasoning
Web-based collaboration
Casual knowledge integration
XDSL
FCM Constructor
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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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