On Thu, Jan 14, 2021 at 1:18 AM, Stan Garfield

<stangarfield@...> wrote:

Mila, thanks for your post. I moved it to start a new topic.

Please provide some additional details on what you mean by "a model of an intelligent knowledge extraction in organizations" so that we can better respond.

On Thu, Jan 14, 2021 at 2:15 AM, Murray Jennex via groups.io

<murphjen@...> wrote:

My journal, International Journal of Knowledge Management, has published several articles on how to use intelligent technologies to do knowledge extraction.  the home page for IJKM is: https://www.igi-global.com/journal/international-journal-knowledge-management/1083,,,,,murray jennex, editor in chief, IJKM

-----Original Message-----
From: Stan Garfield <stangarfield@...>
To: main@SIKM.groups.io
Sent: Wed, Jan 13, 2021 1:04 pm
Subject: [SIKM] Model of an intelligent knowledge extraction in organizations

From: Mila Malekolkalami
Date: Wed, Jan 13, 2021 at 3:58 PM

Hello everyone. Hope you are safe and fine!

I have read all your valuable points in this topic.

Actually, I need some help and hints.

I am working on presenting a model of an intelligent knowledge extraction in organizations.

I don't want to work on the complicated topics such as engineering topics.

What are the main steps to start this task?

What do I have to know?

Can you help me and tell me how I have to start it? I can't find a source that can give me the instructions.

I would be really thankful if you can make it clear for me!

 

Best regards,

Mila

Thank you Stan,

There are different techniques to capture and extract knowledge. I mean tacit and explicit knowledge.

With the development of IT, there are new ways to capture and share knowledge such as data mining that is a technique to extract and discover knowledge from databases.

In the model which I am talking about I don’t want to limit the model to one technique.

I want to know if there is any way to extract automatically all types of knowledge in an organization. Of course supervised by human.

On Thu, Jan 14, 2021 at 1:18 AM, Stan Garfield

<stangarfield@...> wrote:

Mila, thanks for your post. I moved it to start a new topic.

Please provide some additional details on what you mean by "a model of an intelligent knowledge extraction in organizations" so that we can better respond.

Mila,

I am unaware of a technology or even multiple technologies that can 'extract automatically all types of knowledge in an organization,' especially if you mean both tacit and explicit.

At the KM Institute, we fundamentally handle each K mode differently. I'm not an expert on the tech side, since any day you turn your head you can miss a new startup, or the demise of what seemed to be a winner.

On the tacit mode side, where many proven processes exist, techniques have more stability and actually benefit from continuous improvement. Some ignore such techniques, that have been around since the early 2000s or even late 1990s, on the illogical basis that if it is that old, it can't be any good any more.

I have to remind them that the Greeks discovered geometry about 2,500 years ago. Algebra has been known since 3,500 years ago, and popularized by Muslim mathematicians in A.D. 820, who gave Algebra its common name: Al-jabr. 

Back to modern times: The clincher is both that the process/technique (not an IT technology) has been implemented and continuously improved; and that it has been scientifically researched as to its efficacy. It is not just based on ad hoc recommendations, as is often the case with an emerging discipline. 

One of the evidence-based techniques with the highest level of capturing/transferring the most critical K (among many such as exit interviews and mentoring), is a technique we call K Transfer & Retention.

We have a two-day Master class that gives full disclosure and surety of ability to perform by graduation. If an attendee desires certification, they must combine the Master Class as Phase II, along with our robust KM Essentials as Phase I. This results in a Certified K Specialist (CKS) - K Transfer & Retention. 

Contrary to concerns in this forum, the CKS doesn't claim mastery of everything KM, but rather just mastery of a specific technique, which can then be implemented by the certificant.

Douglas.Weidner@...

Chief CKM Instructor

Exec Chairman

On Wed, Jan 13, 2021 at 5:19 PM Mila Malekolkalami via groups.io <Mila_malek_1365=yahoo.com@groups.io> wrote:

Thank you Stan,

There are different techniques to capture and extract knowledge. I mean tacit and explicit knowledge.

With the development of IT, there are new ways to capture and share knowledge such as data mining that is a technique to extract and discover knowledge from databases.

In the model which I am talking about I don’t want to limit the model to one technique.

I want to know if there is any way to extract automatically all types of knowledge in an organization. Of course supervised by human.

On Thu, Jan 14, 2021 at 1:18 AM, Stan Garfield

<stangarfield@...> wrote:

Mila, thanks for your post. I moved it to start a new topic.

Please provide some additional details on what you mean by "a model of an intelligent knowledge extraction in organizations" so that we can better respond.

On Fri, Jan 15, 2021 at 12:45 AM, Douglas Weidner

<douglas.weidner@...> wrote:

Murray,

You didn't clarify whether these hoped-for technologies focus on both tacit and explicit or mostly, even exclusively explicit, which may be about 20% (but growing) of the body of K.

My comments were focused on tacit K, but would of course include explicit as well, which will become known as critical K is transferred.

Douglas Weidner

On Thu, Jan 14, 2021 at 3:53 PM Murray Jennex <murphjen@...> wrote:

there are several technologies being developed to do this.  The problem is that the developers are academics and are experts in the technologies but not KM.  As I get these manuscripts one of the things I have them do is to ensure they relate the use of knowledge extraction technologies to KM and that they understand the concept of knowledge and that they are addressing it correctly.  The papers I'm getting (about a half dozen) are very technical and are mostly pattern based or semantics based approaches.  The technologies are very young and not ready for implementation but give them a year or so and we may see some real advances....murray jennex, eic International Journal of Knowledge Management

-----Original Message-----
From: Douglas Weidner <douglas.weidner@...>
To: main@sikm.groups.io
Sent: Thu, Jan 14, 2021 5:12 am
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

Mila,

I am unaware of a technology or even multiple technologies that can 'extract automatically all types of knowledge in an organization,' especially if you mean both tacit and explicit.

At the KM Institute, we fundamentally handle each K mode differently. I'm not an expert on the tech side, since any day you turn your head you can miss a new startup, or the demise of what seemed to be a winner.

On the tacit mode side, where many proven processes exist, techniques have more stability and actually benefit from continuous improvement. Some ignore such techniques, that have been around since the early 2000s or even late 1990s, on the illogical basis that if it is that old, it can't be any good any more.

I have to remind them that the Greeks discovered geometry about 2,500 years ago. Algebra has been known since 3,500 years ago, and popularized by Muslim mathematicians in A.D. 820, who gave Algebra its common name: Al-jabr. 

Back to modern times: The clincher is both that the process/technique (not an IT technology) has been implemented and continuously improved; and that it has been scientifically researched as to its efficacy. It is not just based on ad hoc recommendations, as is often the case with an emerging discipline. 

One of the evidence-based techniques with the highest level of capturing/transferring the most critical K (among many such as exit interviews and mentoring), is a technique we call K Transfer & Retention.

We have a two-day Master class that gives full disclosure and surety of ability to perform by graduation. If an attendee desires certification, they must combine the Master Class as Phase II, along with our robust KM Essentials as Phase I. This results in a Certified K Specialist (CKS) - K Transfer & Retention. 

Contrary to concerns in this forum, the CKS doesn't claim mastery of everything KM, but rather just mastery of a specific technique, which can then be implemented by the certificant.

Douglas.Weidner@...

Chief CKM Instructor

Exec Chairman

On Wed, Jan 13, 2021 at 5:19 PM Mila Malekolkalami via groups.io <Mila_malek_1365=yahoo.com@groups.io> wrote:

Thank you Stan,

There are different techniques to capture and extract knowledge. I mean tacit and explicit knowledge.

With the development of IT, there are new ways to capture and share knowledge such as data mining that is a technique to extract and discover knowledge from databases.

In the model which I am talking about I don’t want to limit the model to one technique.

I want to know if there is any way to extract automatically all types of knowledge in an organization. Of course supervised by human.

On Thu, Jan 14, 2021 at 1:18 AM, Stan Garfield

<stangarfield@...> wrote:

Mila, thanks for your post. I moved it to start a new topic.

Please provide some additional details on what you mean by "a model of an intelligent knowledge extraction in organizations" so that we can better respond.

Most papers I have read are really technical and I cant find the KM concept in them.

Of course, all of them are based on knowledge, but their approaches and techniques are oit of KM field. I have to be an IT expert to understand most of them.

I have chosen this topic for my thesis because I think the KM position is empty in this way.

Do you think it is possible to present such intelligent framework for knowledge extraction based on the field of KM?

Murray says it takes time to reach that point by new technologies. But can it be possible for us to do something?

On Fri, Jan 15, 2021 at 12:45 AM, Douglas Weidner

<douglas.weidner@...> wrote:

Murray,

You didn't clarify whether these hoped-for technologies focus on both tacit and explicit or mostly, even exclusively explicit, which may be about 20% (but growing) of the body of K.

My comments were focused on tacit K, but would of course include explicit as well, which will become known as critical K is transferred.

Douglas Weidner

On Thu, Jan 14, 2021 at 3:53 PM Murray Jennex <murphjen@...> wrote:

there are several technologies being developed to do this.  The problem is that the developers are academics and are experts in the technologies but not KM.  As I get these manuscripts one of the things I have them do is to ensure they relate the use of knowledge extraction technologies to KM and that they understand the concept of knowledge and that they are addressing it correctly.  The papers I'm getting (about a half dozen) are very technical and are mostly pattern based or semantics based approaches.  The technologies are very young and not ready for implementation but give them a year or so and we may see some real advances....murray jennex, eic International Journal of Knowledge Management

-----Original Message-----
From: Douglas Weidner <douglas.weidner@...>
To: main@sikm.groups.io
Sent: Thu, Jan 14, 2021 5:12 am
Subject: Re: [SIKM] Model of an intelligent knowledge extraction in organizations

Mila,

I am unaware of a technology or even multiple technologies that can 'extract automatically all types of knowledge in an organization,' especially if you mean both tacit and explicit.

At the KM Institute, we fundamentally handle each K mode differently. I'm not an expert on the tech side, since any day you turn your head you can miss a new startup, or the demise of what seemed to be a winner.

On the tacit mode side, where many proven processes exist, techniques have more stability and actually benefit from continuous improvement. Some ignore such techniques, that have been around since the early 2000s or even late 1990s, on the illogical basis that if it is that old, it can't be any good any more.

I have to remind them that the Greeks discovered geometry about 2,500 years ago. Algebra has been known since 3,500 years ago, and popularized by Muslim mathematicians in A.D. 820, who gave Algebra its common name: Al-jabr. 

Back to modern times: The clincher is both that the process/technique (not an IT technology) has been implemented and continuously improved; and that it has been scientifically researched as to its efficacy. It is not just based on ad hoc recommendations, as is often the case with an emerging discipline. 

One of the evidence-based techniques with the highest level of capturing/transferring the most critical K (among many such as exit interviews and mentoring), is a technique we call K Transfer & Retention.

We have a two-day Master class that gives full disclosure and surety of ability to perform by graduation. If an attendee desires certification, they must combine the Master Class as Phase II, along with our robust KM Essentials as Phase I. This results in a Certified K Specialist (CKS) - K Transfer & Retention. 

Contrary to concerns in this forum, the CKS doesn't claim mastery of everything KM, but rather just mastery of a specific technique, which can then be implemented by the certificant.

Douglas.Weidner@...

Chief CKM Instructor

Exec Chairman

On Wed, Jan 13, 2021 at 5:19 PM Mila Malekolkalami via groups.io <Mila_malek_1365=yahoo.com@groups.io> wrote:

Thank you Stan,

There are different techniques to capture and extract knowledge. I mean tacit and explicit knowledge.

With the development of IT, there are new ways to capture and share knowledge such as data mining that is a technique to extract and discover knowledge from databases.

In the model which I am talking about I don’t want to limit the model to one technique.

I want to know if there is any way to extract automatically all types of knowledge in an organization. Of course supervised by human.

On Thu, Jan 14, 2021 at 1:18 AM, Stan Garfield

<stangarfield@...> wrote:

Mila, thanks for your post. I moved it to start a new topic.

Please provide some additional details on what you mean by "a model of an intelligent knowledge extraction in organizations" so that we can better respond.

On Fri, Jan 15, 2021 at 9:37 AM, Murray Jennex via groups.io

<murphjen@...> wrote:

I've actually had a couple of textual intelligent technologies articles so the work is progressing, but I agree it is young and not ready...murray jennex

-----Original Message-----
From: Sam Yip <sam@...>
To: main@SIKM.groups.io
Sent: Thu, Jan 14, 2021 9:41 pm
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. 

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. 

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

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. 

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

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

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.