Re: Model of an intelligent knowledge extraction in organizations #extraction #definition
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In my view, codified knowledge can be seen as BOTH knowledge AND information as far as our management systems are concerned.
It is Information in as much as it is a document, video or other file which can be handled within information management systems, and which therefore falls underneath the umbrella of an information management system.
It is knowledge in as much as it can convey understanding, know-how and the ability to make effective decisions, from one person to another, and therefore falls underneath the umbrella of a knowledge management system (using the term “system” to mean “system of management” rather than IT system).
I know many people see “information” and “knowledge” as two mutual exclusive descriptors, but there is no reason why this should be the case. We are used to thinking this way, but there is no logical basis for it that I can see.
I would therefore submit that the category “codified knowledge” can be seen as a clear example of something that is both categories.
More on the idea here
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Murray Jennex via groups.io
Sent: 16 January 2021 01:36
To: fred@...; main@SIKM.groups.io
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations
at the risk of sounding obnoxious, I have to ask why codified knowledge is information? its still knowledge, just well understood knowledge. For example, just because we well understand the knowledge of how fire works and causes burns does not make that information, it is still knowledge...murray
I agree, Chris. Explicit knowledge is knowledge that has been codified. It is indeed information.
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Chris Collison
I’m with you Fred.
...and from my perspective (which not everyone shares!), I would add that explicit knowledge = information.
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From: main@SIKM.groups.io <main@SIKM.groups.io> on behalf of bill@... <bill@...>
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.
From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Fred Nickols via groups.io
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
Fred Nickols, Consultant
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From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of bill@...
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.”
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From: main@SIKM.groups.io <main@SIKM.groups.io> On Behalf Of Sam Yip via groups.io
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.