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


Murray Jennex
 

this is why I didn't answer the initial question of whether smart knowledge discovery/extraction was addressing tacit and/or explicit knowledge.  Frankly, given that we can't agree on what is tacit and explicit and implicit knowledge I don't use any of those terms, I simply use knowledge since as a editor in chief if I can't point to a solid definition of all these terms it is hard to get people to use them.  Also, from a practical point, I don't really care if knowledge is tacit or explicit or implicit; I just consider knowledge easier or harder to extract and codify.  Also, at the risk of sounding a heretic, I don't think knowledge that can't be stated is knowledge at all, it may be instinct or luck, but if you can't state it in some way (now here I'm fairly liberal as it doesn't have to be in words or documents, it can be visual, or pantomime, or some other explication) it isn't useful, it becomes mystical or metaphysical and I don't see that makes it very useful....murray jennex, eic International Journal of Knowledge Management


-----Original Message-----
From: Chris Collison <chris.collison@...>
To: main@SIKM.groups.io <main@SIKM.groups.io>
Sent: Fri, Jan 15, 2021 12:02 pm
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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