I.Introduction
What is the difference between information, knowledge and expertise?
Such a question is very important because the answer will clear the overlap and confusion most of people suffer from.
Knowledge is the result of intelligent processing done on some input “Information”, and the same is the “Expertise” when intelligent processing is applied on some “Knowledge”, thus its clear now the difference between the three main topics that define the main elements of the field of “Knowledge Engineering”.
The story of “Knowledge Engineering” isn’t from short time, it returns to the beginning of the 20th century when the industry became larger and stronger, needed more labor to be involved, more sophisticated process needed for higher production rate, better quality and, for sure, more profit.
The main industry, in my opinion, that motivated the foundation and establishments for field of “Knowledge Engineering” was the automotive industry.
Automotive industry started by the beginning of the 20th century, this industry need a huge a large number of other fields to incorporate together to made this industry succeed like mechanical engineering, chemicals and metallurgy industries, electrical engineering, sales, marketing, finance, human resources and their management, two main industrial organizations were dominating the market at this time: “Ford” in U.S.A. and Canada and “Daimler – Benz” in Europe, also there were other competitors but they weren’t strong like those organizations, both organizations motivated the researchers and funded them to find the best ways to achieve more success for their organizations..
To manage all these fields together and achieve the integration needed many things should be done, one of the main things needed is to know what they want, what to do, how to do it, how to keep it and how achieve more success in it, one word was the answer “Knowledge”, later they discovered that knowledge isn’t enough and a huge field needed to be established to deal with everything about knowledge, which was “Knowledge Engineering”.
Knowledge, by the time, become very important and critical issue, the more sophisticated the organizations become the more complex knowledge become, organization could be a corporation, educational institute, a research center, a medical center or even the government of a country, and become well known one of the measurements for the strength of any organization is the amount of knowledge the organization have.
But how to get knowledge, from whom, where we can find, why we will need and how will be used, these questions drove the researchers to more deep area that the target isn’t about getting knowledge only or storing knowledge without using it, its more about how this knowledge should be used for the best benefit for the organization, a new hierarchy was founded to organized everything about knowledge: how to manage knowledge, how to store this knowledge and how to get the knowledge you need.
When the researchers get to the knowledge, it was fine and very confusing, especially with huge amounts of knowledge, so the need for standard organizing methodology became a demand to ease the access and understanding the knowledge, so “Knowledge Bases” field was established.
Later they discovered with the increase of the amount of knowledge, knowledge became more confusing even to specialists and the matter is beyond the storage of knowledge in an organized way and the need to put this amount of knowledge into use and not wasting it, so new techniques were developed to organize the knowledge, relations between knowledge topics and elements, resources of knowledge, what to do with it, how to do it, who should do, who can access this knowledge, who should be aware with something specific with this amount or a type of knowledge…etc., all previous questions was stated as the goals for new branch of “Knowledge Engineering” called “Knowledge Management”.
On a later stage it was very important to know how to get the knowledge we want to store and manage?, the matter, as mentioned previously, became more complex and sophisticated and information need to pass a processing stage to achieve knowledge needed for the organization, also when we get this knowledge how to make sure its valid or just correct and not corrupted.
The end of the 1970’s and early 1980’s it became a must to separate between “how to manage knowledge” and “how to get knowledge”, so they sat the rules for a new field under the hierarchy of knowledge engineering called “Knowledge Acquisition”.
II. What is “Knowledge Acquisition”?
Knowledge acquisition, or shortly we will call it KA, is the process of eliciting knowledge from either people or from a process involves and/or results acceptable form of knowledge.
But why we will extract this knowledge from someone or some process?, there should be a result why we want to elicit knowledge or an end product, our end product is to create or provide a contribution for a knowledge base.
Knowledge Acquisition was first established to extract knowledge from expert’s minds, in case they are getting retired or moving to somewhere else, especially when the number of experts in the field is few and their knowledge should be transferred to other people in the organization.
KA processes, in the early days, was done in the organization using interviewing technique to elicit knowledge from experts, but this method wasn’t sufficient enough to elicit all the knowledge needed, also it didn’t show good performance toward validating the elicited knowledge, so the need for more efficient methods and techniques to be introduced to this field, also knowledge need to be categorized and sorted and map the best technique for each type or category of knowledge, so they got to this classification of knowledge:
1.From the point of view of the general frame this knowledge represents:
a.Procedural knowledge: it’s about process, tasks and activities.
b.Conceptual knowledge: it’s about the way things relate to each other and about their properties.
2.From the point of view of the level the knowledge represents:
a.Basic, explicit knowledge.
b.Deep, implicit knowledge.
Also in the early days of KA, KA processes were fully manual processes done by the employees of the organization, a general framework was settled later in the form of steps and procedures to perform KA operation, also they did set aspects for these step procedures to follow.
The KA process consists of 47 procedure step, divided upon four phases, before going through the KA operation phases and steps, the main aspects for procedure step will be explained first, however, not all the aspects mentioned here, just the main aspects because the other aspects are more related to management process or less technical aspects.
2.1.Main aspects for Knowledge Acquisition procedure step
2.1.1.Knowledge capturing
In order to have knowledge acquisition process we should capture this knowledge from either an expert or from a running process, this aspect aim for two main goals:
a.Elicit knowledge.
b.Validate knowledge.
In the following sub-sections knowledge capture main techniques.
2.1.1.1.Interviewing Technique:
This technique is a manual process for questioning the expert; it’s very useful in knowledge elicitation but not efficient in knowledge validation, there are three types of interviewing used:
a.Unstructured interview: little planned and free form chat with the expert.
b.Semi-structured interview: pre-defined questions to the expert, in addition to supplementary questions asked at the interview.
c.Structured interview: pre-defined questions to the expert without any supplementary questions.
2.1.1.2.Modeling Technique:
Using knowledge models to show them to the experts and let them comment, correct or modify any of the fields of the model, this technique is useful in knowledge validation.
2.1.1.3. Specialized Techniques:
Techniques developed by psychologists to elicit knowledge from experts by letting them perform tasks that can reveal knowledge difficult to articulate.
Main goals from knowledge capture techniques are:
a.Focus the expert on the required knowledge only.
b.Help expert to recall knowledge.
c.Help expert to explain knowledge in a clear way.
2.1.2. Knowledge Analysis
Knowledge analysis is a process to perform identification of the elements of knowledge will be entered to the K-base to form its structured and main components; four elements are required to be identified for successful knowledge analysis process:
a.Concepts: things that constitutes a domain, they form main structure of K-base.
b.Values.
c.Attributes.
d.Relations.
“Attribute” and “Values” describe the properties of a concept while the “Relation” element, from its name, describes the relations between concepts and properties of this relation.
2.1.3. Knowledge Modeling
It is the process of creation and use of knowledge models (K-Models) for viewing content of the knowledge base in a clear simple way.
Each K-Model should provide different perspective or different view point to see all different aspects of the knowledge base, main types of K-Model:
a.Tree Model.
b.Matrix Model.
c.Maps.
d.Timelines.
e.Frames.
f.Knowledge pages.
III.Knowledge Acquisition process phases and steps
In this section we will show in brief the four phases of a KA project process, also will go through the steps.
As mentioned before, KA project process consists of 47 procedure step divided between four phases, they are:
I.Phase 1: Start the project, define project scope and plan for the project.
II.Phase 2: Initial capture and modeling for knowledge.
III.Phase 3: a detailed capture and modeling for knowledge elicited.
IV.Phase 4: share and store knowledge.
3.1.Phase 1: Start, Scope and plan for the project
This phase consists of steps 1 to 13, in steps 1 to 4, KA project team will identify the project idea and how this project will benefit the organization, then start to gather opinions from relevant people, create project proposal and get the agree of the key people on the project.
In steps 5 to 8, the project team will create a K-Base to store the future knowledge acquired, in the meantime start to analyze the main topic and decompose it to smaller different topics and rank these topics against key criteria.
In steps 9 to 13, the project team will identify sources of knowledge needed, identify more accurately what sort of project they are doing and define procedures they will follow for the project, using all data from previous steps to create project schedule and build a project plan.
3.2. Phase 2: Initial Capture and Modeling
This phase consists of steps 14 to 29, in steps 14 to 17 the project team will start learning domain basics from documents or free discussion with experts, also they will start preparations for some semi-structured interviews and conducting them, for sure record these interviews and document all data gathered from them.
In steps 18 to 21, analysis for captured knowledge will take place to identify main concepts and create concept tree for the project, validation for this concept tree takes place after with experts and update the project with recent information added after all previous steps.
In steps 22 to 29, project team should create a K-page describing concepts and glossary for all domain terms and expressions then build a meta-model for defining attributes, values for each concept and the relations with other concepts and validate this model with experts.
3.3. Phase 3: Detailed Capture and Modeling
This phase consists of steps 30 to 39, in steps 30 and 31 more deep data capturing process will be conducted for detailed knowledge and creates a finalized main k-model.
In steps 32 to 35 a prototype of the end product of the project and propose it to a sample of end-users and get their feedback for end-product assessment and capturing more deep knowledge by analyzing feedback, in the meantime a completion plan is produced to define actions required to complete the project.
In steps 36 to 39, a cross validation takes place with secondary expert to check models developed before and gather the differences between experts opinions and try to resolve it, finally record all validated knowledge and finalize the K-models and the K-base of the project.
3.4.Phase 4: Share and Store Knowledge
This phase consists of steps 40 to 47, in steps 40 to 43 a format for the end-user is created, using this format and the K-base a semi-final end product is created and presented to end users to conduct a full assessment by them and identify modifications and improvements, then create the final product.
In steps 44 to 47 a final product is released to the market and publicized, after this product is used for a time, its impact on the organization is assessed and documented.
A complete project review takes place to learn lessons and make suggestions for further improvements in methodologies and support the system.
Karim El-Rayes
May 3, 2010
Canada
What is the difference between information, knowledge and expertise?
Such a question is very important because the answer will clear the overlap and confusion most of people suffer from.
Knowledge is the result of intelligent processing done on some input “Information”, and the same is the “Expertise” when intelligent processing is applied on some “Knowledge”, thus its clear now the difference between the three main topics that define the main elements of the field of “Knowledge Engineering”.
The story of “Knowledge Engineering” isn’t from short time, it returns to the beginning of the 20th century when the industry became larger and stronger, needed more labor to be involved, more sophisticated process needed for higher production rate, better quality and, for sure, more profit.
The main industry, in my opinion, that motivated the foundation and establishments for field of “Knowledge Engineering” was the automotive industry.
Automotive industry started by the beginning of the 20th century, this industry need a huge a large number of other fields to incorporate together to made this industry succeed like mechanical engineering, chemicals and metallurgy industries, electrical engineering, sales, marketing, finance, human resources and their management, two main industrial organizations were dominating the market at this time: “Ford” in U.S.A. and Canada and “Daimler – Benz” in Europe, also there were other competitors but they weren’t strong like those organizations, both organizations motivated the researchers and funded them to find the best ways to achieve more success for their organizations..
To manage all these fields together and achieve the integration needed many things should be done, one of the main things needed is to know what they want, what to do, how to do it, how to keep it and how achieve more success in it, one word was the answer “Knowledge”, later they discovered that knowledge isn’t enough and a huge field needed to be established to deal with everything about knowledge, which was “Knowledge Engineering”.
Knowledge, by the time, become very important and critical issue, the more sophisticated the organizations become the more complex knowledge become, organization could be a corporation, educational institute, a research center, a medical center or even the government of a country, and become well known one of the measurements for the strength of any organization is the amount of knowledge the organization have.
But how to get knowledge, from whom, where we can find, why we will need and how will be used, these questions drove the researchers to more deep area that the target isn’t about getting knowledge only or storing knowledge without using it, its more about how this knowledge should be used for the best benefit for the organization, a new hierarchy was founded to organized everything about knowledge: how to manage knowledge, how to store this knowledge and how to get the knowledge you need.
When the researchers get to the knowledge, it was fine and very confusing, especially with huge amounts of knowledge, so the need for standard organizing methodology became a demand to ease the access and understanding the knowledge, so “Knowledge Bases” field was established.
Later they discovered with the increase of the amount of knowledge, knowledge became more confusing even to specialists and the matter is beyond the storage of knowledge in an organized way and the need to put this amount of knowledge into use and not wasting it, so new techniques were developed to organize the knowledge, relations between knowledge topics and elements, resources of knowledge, what to do with it, how to do it, who should do, who can access this knowledge, who should be aware with something specific with this amount or a type of knowledge…etc., all previous questions was stated as the goals for new branch of “Knowledge Engineering” called “Knowledge Management”.
On a later stage it was very important to know how to get the knowledge we want to store and manage?, the matter, as mentioned previously, became more complex and sophisticated and information need to pass a processing stage to achieve knowledge needed for the organization, also when we get this knowledge how to make sure its valid or just correct and not corrupted.
The end of the 1970’s and early 1980’s it became a must to separate between “how to manage knowledge” and “how to get knowledge”, so they sat the rules for a new field under the hierarchy of knowledge engineering called “Knowledge Acquisition”.
II. What is “Knowledge Acquisition”?
Knowledge acquisition, or shortly we will call it KA, is the process of eliciting knowledge from either people or from a process involves and/or results acceptable form of knowledge.
But why we will extract this knowledge from someone or some process?, there should be a result why we want to elicit knowledge or an end product, our end product is to create or provide a contribution for a knowledge base.
Knowledge Acquisition was first established to extract knowledge from expert’s minds, in case they are getting retired or moving to somewhere else, especially when the number of experts in the field is few and their knowledge should be transferred to other people in the organization.
KA processes, in the early days, was done in the organization using interviewing technique to elicit knowledge from experts, but this method wasn’t sufficient enough to elicit all the knowledge needed, also it didn’t show good performance toward validating the elicited knowledge, so the need for more efficient methods and techniques to be introduced to this field, also knowledge need to be categorized and sorted and map the best technique for each type or category of knowledge, so they got to this classification of knowledge:
1.From the point of view of the general frame this knowledge represents:
a.Procedural knowledge: it’s about process, tasks and activities.
b.Conceptual knowledge: it’s about the way things relate to each other and about their properties.
2.From the point of view of the level the knowledge represents:
a.Basic, explicit knowledge.
b.Deep, implicit knowledge.
Also in the early days of KA, KA processes were fully manual processes done by the employees of the organization, a general framework was settled later in the form of steps and procedures to perform KA operation, also they did set aspects for these step procedures to follow.
The KA process consists of 47 procedure step, divided upon four phases, before going through the KA operation phases and steps, the main aspects for procedure step will be explained first, however, not all the aspects mentioned here, just the main aspects because the other aspects are more related to management process or less technical aspects.
2.1.Main aspects for Knowledge Acquisition procedure step
2.1.1.Knowledge capturing
In order to have knowledge acquisition process we should capture this knowledge from either an expert or from a running process, this aspect aim for two main goals:
a.Elicit knowledge.
b.Validate knowledge.
In the following sub-sections knowledge capture main techniques.
2.1.1.1.Interviewing Technique:
This technique is a manual process for questioning the expert; it’s very useful in knowledge elicitation but not efficient in knowledge validation, there are three types of interviewing used:
a.Unstructured interview: little planned and free form chat with the expert.
b.Semi-structured interview: pre-defined questions to the expert, in addition to supplementary questions asked at the interview.
c.Structured interview: pre-defined questions to the expert without any supplementary questions.
2.1.1.2.Modeling Technique:
Using knowledge models to show them to the experts and let them comment, correct or modify any of the fields of the model, this technique is useful in knowledge validation.
2.1.1.3. Specialized Techniques:
Techniques developed by psychologists to elicit knowledge from experts by letting them perform tasks that can reveal knowledge difficult to articulate.
Main goals from knowledge capture techniques are:
a.Focus the expert on the required knowledge only.
b.Help expert to recall knowledge.
c.Help expert to explain knowledge in a clear way.
2.1.2. Knowledge Analysis
Knowledge analysis is a process to perform identification of the elements of knowledge will be entered to the K-base to form its structured and main components; four elements are required to be identified for successful knowledge analysis process:
a.Concepts: things that constitutes a domain, they form main structure of K-base.
b.Values.
c.Attributes.
d.Relations.
“Attribute” and “Values” describe the properties of a concept while the “Relation” element, from its name, describes the relations between concepts and properties of this relation.
2.1.3. Knowledge Modeling
It is the process of creation and use of knowledge models (K-Models) for viewing content of the knowledge base in a clear simple way.
Each K-Model should provide different perspective or different view point to see all different aspects of the knowledge base, main types of K-Model:
a.Tree Model.
b.Matrix Model.
c.Maps.
d.Timelines.
e.Frames.
f.Knowledge pages.
III.Knowledge Acquisition process phases and steps
In this section we will show in brief the four phases of a KA project process, also will go through the steps.
As mentioned before, KA project process consists of 47 procedure step divided between four phases, they are:
I.Phase 1: Start the project, define project scope and plan for the project.
II.Phase 2: Initial capture and modeling for knowledge.
III.Phase 3: a detailed capture and modeling for knowledge elicited.
IV.Phase 4: share and store knowledge.
3.1.Phase 1: Start, Scope and plan for the project
This phase consists of steps 1 to 13, in steps 1 to 4, KA project team will identify the project idea and how this project will benefit the organization, then start to gather opinions from relevant people, create project proposal and get the agree of the key people on the project.
In steps 5 to 8, the project team will create a K-Base to store the future knowledge acquired, in the meantime start to analyze the main topic and decompose it to smaller different topics and rank these topics against key criteria.
In steps 9 to 13, the project team will identify sources of knowledge needed, identify more accurately what sort of project they are doing and define procedures they will follow for the project, using all data from previous steps to create project schedule and build a project plan.
3.2. Phase 2: Initial Capture and Modeling
This phase consists of steps 14 to 29, in steps 14 to 17 the project team will start learning domain basics from documents or free discussion with experts, also they will start preparations for some semi-structured interviews and conducting them, for sure record these interviews and document all data gathered from them.
In steps 18 to 21, analysis for captured knowledge will take place to identify main concepts and create concept tree for the project, validation for this concept tree takes place after with experts and update the project with recent information added after all previous steps.
In steps 22 to 29, project team should create a K-page describing concepts and glossary for all domain terms and expressions then build a meta-model for defining attributes, values for each concept and the relations with other concepts and validate this model with experts.
3.3. Phase 3: Detailed Capture and Modeling
This phase consists of steps 30 to 39, in steps 30 and 31 more deep data capturing process will be conducted for detailed knowledge and creates a finalized main k-model.
In steps 32 to 35 a prototype of the end product of the project and propose it to a sample of end-users and get their feedback for end-product assessment and capturing more deep knowledge by analyzing feedback, in the meantime a completion plan is produced to define actions required to complete the project.
In steps 36 to 39, a cross validation takes place with secondary expert to check models developed before and gather the differences between experts opinions and try to resolve it, finally record all validated knowledge and finalize the K-models and the K-base of the project.
3.4.Phase 4: Share and Store Knowledge
This phase consists of steps 40 to 47, in steps 40 to 43 a format for the end-user is created, using this format and the K-base a semi-final end product is created and presented to end users to conduct a full assessment by them and identify modifications and improvements, then create the final product.
In steps 44 to 47 a final product is released to the market and publicized, after this product is used for a time, its impact on the organization is assessed and documented.
A complete project review takes place to learn lessons and make suggestions for further improvements in methodologies and support the system.
Karim El-Rayes
May 3, 2010
Canada
