Re: Model of an intelligent knowledge extraction in organizations #extraction #definition


Tim Powell
 

Good question.  As I see it, once knowledge is elicited/codified, it is (by definition) information (e.g., in a database, report, etc.).  It can then be converted back into (someone else’s) knowledge – by consulting the database, reading the report, and so on.  This is a very noisy and inexact process (first described but Claude Shannon) – but it’s what we have.  I call it the “K-I-K translation,” others call it “writing and reading.”

 

Knowledge usage (in making decisions, formulating and taking actions, and ultimately producing value) I see as separate from, and subsequent to, the production of knowledge.  And usage is the only path to value.

 

Said another way, yes it is (unfortunately) possible to have knowledge that produces little or no value.  It happens all the time, in fact – and identifying and fixing that condition is what has over time become a focus (obsession?) of mine.

 

tp


TIM WOOD POWELL 
| President, The Knowledge Agency® Author, The Value of Knowledge

New York City, USA | DIRECT/MOBILE +1.212.243.1200 | ZOOM 212-243-1200

SITE www.KnowledgeAgency.com | BLOG www.KnowledgeValueChain.com

 

 

From: <main@SIKM.groups.io> on behalf of "Mila Malekolkalami via groups.io" <Mila_malek_1365@...>
Reply-To: "main@SIKM.groups.io" <main@SIKM.groups.io>
Date: Friday, January 15, 2021 at 4:22 PM
To: "main@SIKM.groups.io" <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in o

 

How is explicit knowledge information?

We have information. But if we don't put it in a proper process and use it, it is not knowledge. 

It is useless. The information which is not used can't be knowledge, can it?

I think these 2 are related to each other. If there is no information, so there isn't any knowledge.



 

On Sat, Jan 16, 2021 at 12:38 AM, Tim Powell

<tim.powell@...> wrote:

I’m so glad, Bill, Chris, and Fred that you’ve mentioned that “explicit knowledge” is actually information.  When I wrote this in my latest book, I predicted that it would be received in some quarters as heresy — but am reassured that others have independently arrived at a similar conclusion.

 

The failure to distinguish between knowledge and information is, in my experience, one of the two biggest stumbling blocks to the effective managing of knowledge.  Both K and I are critically important — but are very different for reasons I go into. 

 

In short, information about knowledge — essentially, that which we are seeking to elicit and/or codify — is crucial as an index or trace of the human knowledge it represents.  It must not, however, be conflated with the knowledge itself -- which remains (as Drucker first described in 1964) perpetually captive within human brains.  “The map is not the territory.”

 

My opinions,

 

Tim


TIM WOOD POWELL 
| 
President, The Knowledge Agency® Author, The Value of Knowledge

New York City, USA | DIRECT/MOBILE +1.212.243.1200 | ZOOM 212-243-1200

SITE www.KnowledgeAgency.com | BLOG www.KnowledgeValueChain.com

 

 

From: <main@SIKM.groups.io> on behalf of Fred Nickols <fred@...>
Reply-To: "main@SIKM.groups.io" <main@SIKM.groups.io>
Date: Friday, January 15, 2021 at 3:24 PM
To: "main@SIKM.groups.io" <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

I agree, Chris.  Explicit knowledge is knowledge that has been codified.  It is indeed information.

 

Fred Nickols

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Chris Collison
Sent: Friday, January 15, 2021 3:03 PM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

I’m with you Fred.

...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.

Cheers,

Chris

 


From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
Sent: Friday, January 15, 2021 7:57:21 PM
To: main@SIKM.groups.io <main@SIKM.groups.io>
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Hi Fred

 

I say the following knowing the risk with which I am placing myself 😊…never been a fan of “implicit” and so I am not a fan of Polanyi’s definition.  My point, based on years of practical consulting application, is that “tacit” knowledge can be elicited, harvested, captured and distilled to make it searchable, findable, accessible and usable/reusable. There is a skill and craft involved IMO. My definition of knowledge is based on the concept that it consists of information and experience = knowledge. For practical, not academic or theoretical application, this simple, practical definition I find has meaning in a business or operational environment.  This has been my experience.

 

Best

 

Bill

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
Sent: Friday, January 15, 2021 11:22
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

FWIW, in relation to Bill’s use of “explicit” and “tacit,” I find a third category useful; namely, “implicit.”  I define “implicit” as knowledge which hasn’t been articulated but can be.  Tacit, by Polanyi’s definition, can’t be articulated.  And explicit, of course is knowledge that has been articulated.

 

When it comes to capturing implicit knowledge, lots of folks have been doing that for many years.  You will find them in the training and human performance community.

 

For more, see a piece I wrote for the KM Yearbook (2000-2001).  https://www.nickols.us/knowledge_in_KM.pdf

 

Regards,

 

 

Fred Nickols, Consultant

 

My Objective is to Help You Achieve Yours

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
Sent: Friday, January 15, 2021 10:41 AM
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

Dear Colleagues

 

Been enjoying this timeless discussion on K capture and the extended inference about tacit knowledge and the subtleties of human language in the sharing in the context of the below thread.

 

Believe it is generally recognized that technology cannot yet get what is one person’s mind and transfer it to another person’s mind. At the heart of the issue, I believe, is the skill set that is required to capture or better said, to elicit what someone knows or can share, to distill and make sense of that knowledge whether it be explicit or tacit, and then to characterize it for reuse within an organization (fit for purpose and context relevant) and then to make is searchable, findable, downloadable, and usable/reusable.

I believe that this is a reasonable approach to as stated below “…to study KM processes, identify patterns and see if technology can address that in a narrow sense.” 

Best

 

Bill

 

 

  

 

Learn more about the solutions and value we provide at www.workingknowledge-csp.com

 

 

 

 

From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
Sent: Thursday, January 14, 2021 21:41
To: main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

 

We are still very far from seeing any AI technology that can intelligently extract human knowledge in a broad sense. A main challenge is that of data, and in particular textual data.  Data has to be structured in a certain format to be consumed by an AI machine, but in the case of textual data, the number of permutation and subtleties in human language makes this task tremendously difficult. But perhaps the biggest challenge is the limitation of computation power. The most powerful computer today has roughly 1 billion neural connections/synapses, similar to the amount of synapses that a honey bee has vs 100-150 trillion synapses of a human brain. It has been suggested by computer scientists that we are actually just approaching bee-level intelligence, and we are not even there yet. 

That said, AI should be sophisticated enough for a focussed application, especially if it’s built around narrow use cases and trained for specific domains. e.g. the technology to recognise and extract names/entities from homogenous, unstructured text is fairly advanced, and so is the ability for a machine to auto-index/classify documents and textual data. Information extracted as such, combined with data visualisation techniques, will go a long way in enhancing the comprehension of what’s contained in an organization's textual data, and that is a possible KM angle you can look into. 

Instead of looking at broad ways “to extract automatically all types of knowledge in an organization” (I don't think this exists), it might be worthwhile to study KM processes, identify patterns and see if technology can address that in a narrow sense. 

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