2001-2003 [email protected] ELITE ENTERPRISES AND KM.

2001-2003 [email protected]

ELITE ENTERPRISES

AND KM


Data

Information

Knowledge

Wisdom


Grid with asymmetric atomic sizeGrid with uniform atomic sizeGrid with varying asymmetric atomic size.• (this last guy must be the right guy, therefore, microscopic

atomic size should be built in. • The different models that use the “layer of understanding” will

determine what granularity they need.

grid of atomic concepts of uniform size;but how atomic should they be?

Scott A. Carpenter, 30-Oct-02-Wed-13:33pm


Grid with varying asymmetric atomic size.•(this last guy must be the right guy, therefore, microscopic atomic size should be built in.

•The different models that use the “layer of understanding” will determine what granularity they need.

2001-2003 [email protected] A. Carpenter, 04-Jan-03-Sat-09:13 am

Taxonomies within taxonomies…how fine?

Probably need to take it down to the individual word as does “Applied Semantics”.

FAST KB

Activity

SubWorkPlan or Test

Element

Hmmm, probably adding more and more redundancy as we move to the smaller taxonomies, as a particular Element (and especially a word or word phrase) is likely to be used in many larger taxonomic units. Therefore, we need an idea information base that feeds this base, and we need an element/word database to feed the information base. Thus, Applied Semantics provides the database. ALL tacit information is captured in the information base, and the tacit knowledge is captured in the knowledge base (FAST).


Document RepositoryUnorganized Information Base

Organized Database (micro micro taxonomy) Applied SemSemi-organized Information Base (micro taxonomy) FAST

Organized Knowledge Base (macro taxonomy) FAST

Scott A. Carpenter, 010-Jan-03-Fri-08:31 am


Document RepositoryUnorganized Information Base containing

independent concepts which are numbered 1-to-N

Organized Database (micro micro taxonomy) Applied SemSemi-organized Information Base (micro taxonomy) FAST

Organized Knowledge Base (macro taxonomy) FAST

1 2 3 … … N 1 2 3 … … N


Back EndStructure Model

DTD/SchemaVocuabularyTaxonomy

KnowledgeEngineers

Back EndGoal Model

DTD/SchemaTaxonomy

SME, Practice Manager

Document RepositoryContains unorganized and

unrationalized information, knowledge, and wisdom

KB Wisdom

WISDOM SYSTEM

I’m trying to figure out how to get applied semantics technology in here; the micro-micro taxonomy concept. What I think Applied Semantics can do is to facilitate conversion of all document repositories into KBs and provide Wisdom Systems!

Scott A. Carpenter, 04-Jan-03-Sat-09:12 am


DATA~100,000 unicode

ideograms for English, and

growing slowly.

INFOApplied Semantics 1,000,000 concepts

for English and growing rapidly

KNOWLEDGE?

Scott A. Carpenter, 04-Jan-03-Sat-10:11 am


Cognitive Paradigm for Learning

Environment (Reality)

Deep abstraction

Shallow abstraction

SimplicityOrder

Understanding

Model

Goal

Wisdom

Mission

Vision

Data

Index

Model

Equations

Goal

Mission

Vision

Data

Index

Information

Knowledge

Complexity Chaos

Mystery

?

?

ontology

taxonomy

index

?



Deep abstraction

Shallowabstraction

Wisdom

Data

Index

Model

Equations

Goal

Information

Knowledge

Mission

Vision

ItemItem

FactFact

SolnSoln

Achievement

Achievement

GrowthGrowth

Scott A. Carpenter, 20-Jan-2003-Mon-23:19 pm



Deep abstraction

Shallowabstraction

Simplicity Order

Wisdom

Data

Index

Model

Equations

Goal

Information

Knowledge

Complexity Mystery

Mission

Vision

Reaction rate parameter data fields

<rr> = f(temp)

Plasma physics(countour space pic)

Radiation losses

T-vs-<rr> index

<rr> varies exponentially with temp

Maximize SpJetPwr

Operate with <rr> = x and T = y because…

Systems Thinking

Component Thinking




Deep abstraction

Shallowabstraction

Simplicity Order

Wisdom

Data

Index

Model

Equations

Goal

Information

Knowledge

Complexity Mystery

Mission

Vision

Reaction rate parameter data fields

<rr> = f(temp)

Plasma physics(countour space pic)

Radiation losses

T-vs-<rr> index

<rr> varies exponentially with temp

Maximize SpJetPwr

Operate with <rr> = x and T = y because…

Need to add more Data boxes with other parameters to support information box and K box.


Measures:1:2:32ab:3a+3b2bc:3b+3c2ac:3a+3c• These ratios should be measurables, but this means that

we have to have a system that can measure it accurately, not ficticiously.

• This picture looks pretty bad, as resources are not well shared.

• These ratios should be part of the VPs and their underlying departments Annual evaluations and profit sharing, so that it becomes the bane of all individuals to contirbute to automated and transparent system and work process and protections (IP).

• VPs don’t know very well the force structure and expertise of the other VPs, therefore, they tend to NOT rely on other’s expertise, but rather to build a reliable known expertise within their own particular organization. Thus, we need to encourage everyone to make each VPs resources known throughout the organization, to measure that, and to communicate that, and then encourage everyone to contribute to automation and transparency and global decision support systems.

• We want to be able to reallocate costly human intelligence to create unique-value growth expertise and service and product value and intelligence delivery value!! That’s what our company is.

• Also provides for visibility of macro and micro strategies, and thus feedback on them and justification.

12bc

2ab

2ac

3c

3b3a

VPb ProfS

VPaEntS

VPc EduS



• I can make an equation for this thing that shows how saving redundant staff will allow allocation of resources to expand value-add intelligence expertise and product and service value adds!!! Max ROI.

• Notice how the overlap has increased! This means the measured ratios are improving.

• Now the VPs might say, wow, as we come to align our processes, perhaps it become simple enough for consolidation into only two VPs, or even 1 VP. Therefore, there must be incentive to automate and transparentize…the benefit to individuals would be annual bonuses and “new” challenge projects!!

• Scott, develop an equation set for this, with valuations, and then write a paper around it!!!!

12bc

2ab

2ac

3c

3b3a

VPb ProfS

VPaEntS

VPc EduS

12bc

2ab

2ac

3c

3b3a

VPb ProfS

VPaEntS

VPc EduS



Vision initiates the learning process by building the Templates, and thereafter works to extend

them.

A small chunk of Environment, filtered through an Index, yields Data

Data, filtered through Rules, yields Information

Information, filtered through a Model, yields Knowledge

Knowledge, filtered through a Goal, yields Wisdom

Vision

Wisdom, filtered through a Mission, yields Vision

IndexEnvironment

(a small chunk)

ModelInformation

What happens in the

environment.

Mission

Rules

Goals

Wisdom(best

practice)

How to use the

environment.

Data

What is in the

environment.

Knowledge(practice)

Why it happens in

the environment.

Imagination is more important than knowledge. - A. Einstein


DOMAIN THINK & COMMUNICATION& SEMANTIC WEB

& WORDS, MEANINGS,

VALUES


Words

Common Understanding

Common Values

Unicode Accuracy

Xlation accuracy / Culture context is different, even if the language is common there will be some discrepancy. Common human context is key, and will likely be a focus of future technology development.

Common Values

Scott A. Carpenter, 20-Jan-2003-Mon-23:11 pm


Environment

DATA

VISION

INFORMATION

KNOWLEDGE

WISDOM

ENVIRONMENT(Complexity &

Mystery)

Mission

Goal

Model

Rules

Index

DATA

VISION

INFORMATION

KNOWLEDGE

WISDOM

Mission

Goal

Model

Rules

Index

A concise idea originates here, having a finite expanse (interpretation, scope)

Same overall system, but in a different language, and thus leads to

possibly different understanding – certainly the fuzziness has grown.

ENVIRONMENT(Complexity &

Mystery)

Grapheme 1

Syntax 1

Grapheme 2

Syntax 2

Lang 1Lang 2

Lang 1Lang 2

MindLang 2 MindLang 1

This is a paper! Scott A. Carpenter, 26-Oct-02-Sat-11:54

Carpenter Communication Paradigm (unicode must understand words, meanings, values)

Person has a new idea. This person extrapolates their goal list, and the idea “appears” to solve their mission (feasibility). Person then expands their model (generalization), and the idea “appears” to be a feasible solution. The rules are specifically described structurally that “appear” to make the model work. Then the index is expanded (vocabulary, unicode) and the data is obtained from the environment. How are each of these layers described by the person, by the system? As the concepts work their way down into atomic “understanding”, and then back up again in a different language? The uncertainty in understanding must grow! This implies that none of us will be standing on the same layers of understanding, as each of us will understand each layer slightly differently than anyone else. Fascinating. Note: In the figure below, the grapheme, syntax, and language might also affect the index, rules, model…


Unicode URI

RDF + rdfschema

XML + NS + xmlschema

These markup languages provide for term specification (I.e. resource definition).

Ontology Layer

Logic Layer

Scott, map this to your 6-layer model of human cognition.

Term definition and term relationships.

Deduce the implications of term definitions and term relationships; that is, add new information or knowledge.

Environment / UniverseWell of infinite mystery and complexity


Words Meaning

Values

Shared Symbols

Shared Understanding

Shared Values

Language Context

Actionable

Useful Output

Sender Receiver


Shared Symbols

Shared Context

Shared Values

UnderstandingLanguage

Teamwork

Communication

ROC

Return on Communication


Shared Symbols

Shared Context

UnderstandingLanguage

Environmentalist Nuclear Engineer

English

Clean the Earth

Kill Nuclear Power!

English

Clean the Earth

Build Nuclear Electric!

Symbols

Context

Values

Shared Values

Teamwork


Technology


Technology

MarketingResearch

IntellectualProperty

Ethics

InformationSystems

Advertising

AssetManagement

StrategicAnalysis

ManagingSkills

WorkforceMotivation

Economics

FinancialManagement

Acquisitions, Mergers

RiskManagement

Hedging

Ergonomics

Litigation

MaximizeShareholder

Wealth

Intra-Organizational

Culture

Inter-Organizational

Culture

Communication

MarketAnalysis

Trans-nationalism


Risk Managemen

t

Who’s Perspective? What Scope?


Maximize Shareholder Wealth

Research

Risk Manageme

nt

Communication

Corporate Morale

Ergonomics

Workforce Diversity

Advertising

Market Analysis

Litigation

Business Models

Competition

Economics

Ethics

Acquisition & Mergers

Environment pollution

Financial Manageme

nt

Technology

HedgingInformation

Systems

Strategic Analysis

Trans-nationalis

m

Enterprise Resource Planning

Intellectual Property

2001-2003 [email protected] ELITE ENTERPRISES AND KM.

Documents

Transcript of 2001-2003 [email protected] ELITE ENTERPRISES AND KM.