Knowledge Management Systems, Technologies and Tools I
• KM technologies
Knowledge Management requires
Knowledge Management Systems, Technologies and Tools I
• KM technologies
Knowledge Management requires technologies to support the new strategies, processes, methods and techniques to better create, disseminate, share and apply the best knowledge, anytime and anyplace, across the team, across teams, across the organisation and across several organisations, especially its clients, customers, partners, suppliers and other key stakeholders.
The key technologies are communication and collaboration technologies that are web based for internet and intranet usage, as well as mobile technologies such as PDA’s, PC’s, telephone and videoconferencing. New technologies are rapidly emerging that act as intelligent agents and assistants to search, summarise, conceptualise and recognise patterns of information and knowledge.
For an effective KM initiative across the organisation, there needs to be in place, at least:
â–ª Knowledge Portal
There is often confusion between the terms ‘information portal’ and ‘knowledge portal’.
An information portal is often described as a gateway to information to enable the user to have one, more simplified way of navigating towards the desired information.
However a ‘knowledge portal’ is far more than an information portal because, as well as information navigation and access, it contains within it software technologies to, at least, support the processes of virtual team communication and collaboration and software technologies to support the 9 step process of managing knowledge. Furthermore, it contains intelligent agent software to identify and automatically distribute information and knowledge effectively to knowledge workers based on knowledge profiling.
â–ª Knowledge Profiles
Within the knowledge portal, each knowledge worker can update and maintain a personal ‘knowledge profile’ which identifies his/her specific knowledge needs, areas of interest and frequency of distribution.
â–ª Collaborative workspaces
Within the knowledge portal, shared work spaces can be set up for each new team or project. These will become knowledge repositories from which new knowledge will be distilled regularly and systematically and shared across other teams in the organisation. Within the shared and collaborative workspace, at least, the following communication and collaboration functions could be performed:
â–ª Urgent requests
Within the knowledge portal, it is very useful to have a facility and underlying process to enter any ‘Urgent Request’ into the portal and receive back any responses from across the organisation. Rather than needing to know ‘who might know’ the request is entered blindly and responses will be made if it is known in the organisation and people are willing to support and respond to this activity. This is a very effective way of better leveraging the knowledge across the organisation.
â–ª Document Libraries
The document library is typically the location where all documents are stored. The library should be context relative and allow the ease of control over any document type. Many organisations now employ an Electronic Document and Records Management System (EDRMS) for this requirements but the integration of the EDRMS with all other relevant information and knowledge sources is imperative.
â–ª Knowledge Server and services
In order to foster knowledge networking across the entire organisation and support knowledge processes for creating, retaining, leveraging, reusing, measuring and optimising the use of the organisational knowledge assets, a centralised knowledge server is required that will:
â–ª manage the communications and collaboration between networks of people
â–ª enable the access, creation and sharing of knowledge between them
The centralised knowledge server will manage the processes and knowledge services that generate and disseminate knowledge assets.
The key components of a generic knowledge server are:
â–ª a knowledge portal interface designed around a knowledge asset schema (see KM consulting section) as a gateway to user access, security and applications
â–ª Knowledge banks
â–ª Advanced search capabilities â–ª collaboration services â–ª search and discovery services â–ª publishing services based on user knowledge needs and knowledge profiling â–ª a knowledge map (taxonomy) â–ª knowledge repository for information and process management â–ª Text summarising and conceptualising â–ª Intelligent agentware â–ª an Intranet infrastructure for integrated email, file servers, internet/intranet services
Knowledge Bases (Banks)
For each key knowledge area identified, there needs to be a Knowledge Base.
A Knowledge Base contains:
â–ª both structured and unstructured discussion forums
▪ rich ‘knowledge objects’ that have been designed for the efficient and effective transfer of knowledge using multimedia, video, audio
â–ª embedded communications theory (eg storytelling)
â–ª KM processes to:
â–ª critically review knowledge nominations and turn them into improved knowledge
â–ª automatically find and publish knowledge to users according to users knowledge profiles
â–ª transfer knowledge effectively
• Artificial intelligence as a facilitating technology for knowledge management
AI is “buzzword†in the industry today. However, AI has been around for decades. The intent of AI is to enable computers to perform tasks that normally require human intelligence, as such AI will evolve to take many jobs once performed by humans.
KM and AI are at their core about knowledge. AI provides the mechanisms to enable machines to learn. AI allows machines to acquire, process and use knowledge to perform tasks and to unlock knowledge that can be delivered to humans to improve the decision-making process.
AI and KM are closely related. KM allows an understanding of knowledge to occur, while AI provides the capabilities to expand, use, and create knowledge in ways we have not yet imagined.
The connection of KM and AI has lead the way for cognitive computing.
Cognitive computing uses computerized models to simulate human thought processe–s. Cognitive computing involves self/deep learning artificial neural network software that use text/data mining, pattern recognition and natural language processing to mimic the way the human brain works. Cognitive computing is leading the way for future applications involving AI and KM.
In recent years, the ability to mine larger amounts of data, information and knowledge to gain competitive advantage and the importance of data and text analytics to this effort is gaining momentum. As the proliferation of structured and unstructured data continues to grow we will continue to have a need to uncover the knowledge contained within these big data resources. Cognitive computing will be key in extracting knowledge from big data. Strategy, process centric approaches and interorganizational aspects of decision support to research on new technology and academic endeavors in this space will continue to provide insights on how we process big data to enhance decision making.
• Knowledge capture systems and knowledge engineering
Knowledge Capture Systems support the process of retrieving either explicit or tacit knowledge that resides within people, artifacts, or organizational entities. These systems can help capture knowledge that resides within or outside organizational boundaries including within consultants, competitors, customers, suppliers, and prior employers of the organization’s new employees.
The earliest mechanisms for knowledge capture dates to the anthropological use of stories – the earliest form of art, education and entertainment. Storytelling is the mechanism by which early civilizations passed on their values and their wisdom from one generation to the next.
The importance of using metaphors and stories as a mechanism for capturing and transferring tacit knowledge is increasingly drawing the attention of organizations.
Technology can also facilitate capturing the knowledge of experts. One type of knowledge capture system is based on the use of concept maps as a knowledge-modeling tool. A concept map is a diagram showing the relationships among concepts.
In the 1960s, Joseph D. Novak (1993) at Cornell University began to study the concept mapping technique. His work was based on the theories of David Ausubel (1968), who stressed the importance of prior knowledge in being able to learn about new concepts. Novak concluded that “Meaningful learning involves the assimilation of new concepts and propositions into existing cognitive structures.”
A concept map is a graphical representation where nodes (points or vertices) represent concepts, and links (arcs or lines) represent the relationships between concepts. The concepts, and sometimes the links, are labelled on the concept map. The links between the concepts can be one-way, two-way, or non-directional. The concepts and the links may be categorized, and the concept map may show temporal or causal relationships between concepts (Plotnick, 1997).
Concept maps provide an effective methodology to organize and structure the concepts representing the expert’s domain knowledge.
Knowledge engineering
Knowledge engineering is a field of artificial intelligence ( AI) that tries to emulate the judgment and behavior of a human expert in a given field.
Knowledge engineering is the technology behind the creation of  expert systems to assist with issues related to their programmed field of knowledge. Expert systems involve a large and expandable  knowledge base integrated with a rules  engine that specifies how to apply information in the  knowledge base to each particular situation. The systems may also incorporate  machine learning so that they can learn from experience in the same way that humans do. Expert systems are used in various fields including healthcare, customer service, financial services, manufacturing and the law.
Using  algorithms to emulate the thought patterns of a subject matter expert, knowledge engineering tries to take on questions and issues as a human expert would. Looking at the structure of a task or decision, knowledge engineering studies how the conclusion is reached. A library of problem-solving methods and a body of collateral knowledge are used to approach the issue or question. The amount of collateral knowledge can be very large. Depending on the task and the knowledge that is drawn on, the virtual expert may assist with  troubleshooting, solving issues, assisting a human or acting as a virtual agent.
Scientists originally attempted knowledge engineering by trying to emulate real experts. Using the virtual expert was supposed to get you the same answer as you would get from a human expert. This approach was called the transfer approach. However, the expertise that a specialist required to answer questions or respond to issues posed to it needed too much collateral knowledge: information that is not central to the given issue but still applied to make judgments.
A surprising amount of collateral knowledge is required to enable analogous reasoning and nonlinear thought. Currently, a modeling approach is used where the same knowledge and process need not necessarily be used to reach the same conclusion for a given question or issue. Eventually, it is expected that knowledge engineering will produce a specialist that surpasses the abilities of its human counterparts.
• How knowledge sharing systems help users share their knowledge, both tacit and explicit
Knowledge Sharing Systems support the process through which explicit or tacit knowledge is communicated to other individuals. These systems are also referred to as knowledge repositories.
The two types of explicit knowledge sharing systems most widely discussed in the KM literature are:
lessons learned and
expertise locator systems.
Systems that support tacit knowledge sharing are those typically utilized by communities of practice.
Corporate Memory (also known as an organizational memory) is made up of the aggregate intellectual assets of an organization. It is the combination of both explicit and tacit knowledge. The loss of Corporate Memory often results from a lack of appropriate technologies for the organization and exchange of documents. Another contributing factor to the loss of corporate memory is the departure of employees because of either turnover or retirement. KM is concerned with developing applications that will prevent the loss of corporate memory.
Knowledge sharing systems are classified according to their attributes
· Incident report databases
· Alert systems
· Best practices databases
· Lessons-learned systems
Incident report databases are used to disseminate information related to incidents or malfunctions. Incident reports typically describe the incident together with explanations of the incident, although they may not suggest any recommendations.
Alert systems were originally intended to disseminate information about a negative experience that has occurred or is expected to occur. Alert systems could be used to report problems experienced with technology, such as an alert system that issues recalls for consumer products.
Best practices databases describe successful efforts, typically from the reengineering of business processes that could be applicable to organizational processes. Best practices differ from lessons learned in that they capture only succesful events, which may not be dervied from experience.
The goal of lessons-learned systems is to capture and provide lessons that can benefit employees who encounter situations that closely resemble a previous experience in a similar situation. LLS could be pure repositories of lessons or be sometimes intermixed with other sources of information.
Expertise-Locator Systems are knowledge repositories that attempt to organize knowledge by identifying experts who possess specific knowledge. Expertise locator systems are also known as expert directories, expertise directories, skill directories, skills catalogues, white pages or yellow pages.
, processes, methods and techniques to better create, disseminate, share and apply the best knowledge, anytime and anyplace, across the team, across teams, across the organisation and across several organisations, especially its clients, customers, partners, suppliers and other key stakeholders.
The key technologies are communication and collaboration technologies that are web based for internet and intranet usage, as well as mobile technologies such as PDA’s, PC’s, telephone and videoconferencing. New technologies are rapidly emerging that act as intelligent agents and assistants to search, summarise, conceptualise and recognise patterns of information and knowledge.
For an effective KM initiative across the organisation, there needs to be in place, at least:
â–ª Knowledge Portal
There is often confusion between the terms ‘information portal’ and ‘knowledge portal’.
An information portal is often described as a gateway to information to enable the user to have one, more simplified way of navigating towards the desired information.
However a ‘knowledge portal’ is far more than an information portal because, as well as information navigation and access, it contains within it software technologies to, at least, support the processes of virtual team communication and collaboration and software technologies to support the 9 step process of managing knowledge. Furthermore, it contains intelligent agent software to identify and automatically distribute information and knowledge effectively to knowledge workers based on knowledge profiling.
â–ª Knowledge Profiles
Within the knowledge portal, each knowledge worker can update and maintain a personal ‘knowledge profile’ which identifies his/her specific knowledge needs, areas of interest and frequency of distribution.
â–ª Collaborative workspaces
Within the knowledge portal, shared work spaces can be set up for each new team or project. These will become knowledge repositories from which new knowledge will be distilled regularly and systematically and shared across other teams in the organisation. Within the shared and collaborative workspace, at least, the following communication and collaboration functions could be performed:
â–ª Urgent requests
Within the knowledge portal, it is very useful to have a facility and underlying process to enter any ‘Urgent Request’ into the portal and receive back any responses from across the organisation. Rather than needing to know ‘who might know’ the request is entered blindly and responses will be made if it is known in the organisation and people are willing to support and respond to this activity. This is a very effective way of better leveraging the knowledge across the organisation.
â–ª Document Libraries
The document library is typically the location where all documents are stored. The library should be context relative and allow the ease of control over any document type. Many organisations now employ an Electronic Document and Records Management System (EDRMS) for this requirements but the integration of the EDRMS with all other relevant information and knowledge sources is imperative.
â–ª Knowledge Server and services
In order to foster knowledge networking across the entire organisation and support knowledge processes for creating, retaining, leveraging, reusing, measuring and optimising the use of the organisational knowledge assets, a centralised knowledge server is required that will:
â–ª manage the communications and collaboration between networks of people
â–ª enable the access, creation and sharing of knowledge between them
The centralised knowledge server will manage the processes and knowledge services that generate and disseminate knowledge assets.
The key components of a generic knowledge server are:
â–ª a knowledge portal interface designed around a knowledge asset schema (see KM consulting section) as a gateway to user access, security and applications
â–ª Knowledge banks
â–ª Advanced search capabilities â–ª collaboration services â–ª search and discovery services â–ª publishing services based on user knowledge needs and knowledge profiling â–ª a knowledge map (taxonomy) â–ª knowledge repository for information and process management â–ª Text summarising and conceptualising â–ª Intelligent agentware â–ª an Intranet infrastructure for integrated email, file servers, internet/intranet services
Knowledge Bases (Banks)
For each key knowledge area identified, there needs to be a Knowledge Base.
A Knowledge Base contains:
â–ª both structured and unstructured discussion forums
▪ rich ‘knowledge objects’ that have been designed for the efficient and effective transfer of knowledge using multimedia, video, audio
â–ª embedded communications theory (eg storytelling)
â–ª KM processes to:
â–ª critically review knowledge nominations and turn them into improved knowledge
â–ª automatically find and publish knowledge to users according to users knowledge profiles
â–ª transfer knowledge effectively
• Artificial intelligence as a facilitating technology for knowledge management
AI is “buzzword†in the industry today. However, AI has been around for decades. The intent of AI is to enable computers to perform tasks that normally require human intelligence, as such AI will evolve to take many jobs once performed by humans.
KM and AI are at their core about knowledge. AI provides the mechanisms to enable machines to learn. AI allows machines to acquire, process and use knowledge to perform tasks and to unlock knowledge that can be delivered to humans to improve the decision-making process.
AI and KM are closely related. KM allows an understanding of knowledge to occur, while AI provides the capabilities to expand, use, and create knowledge in ways we have not yet imagined.
The connection of KM and AI has lead the way for cognitive computing.
Cognitive computing uses computerized models to simulate human thought processe–s. Cognitive computing involves self/deep learning artificial neural network software that use text/data mining, pattern recognition and natural language processing to mimic the way the human brain works. Cognitive computing is leading the way for future applications involving AI and KM.
In recent years, the ability to mine larger amounts of data, information and knowledge to gain competitive advantage and the importance of data and text analytics to this effort is gaining momentum. As the proliferation of structured and unstructured data continues to grow we will continue to have a need to uncover the knowledge contained within these big data resources. Cognitive computing will be key in extracting knowledge from big data. Strategy, process centric approaches and interorganizational aspects of decision support to research on new technology and academic endeavors in this space will continue to provide insights on how we process big data to enhance decision making.
• Knowledge capture systems and knowledge engineering
Knowledge Capture Systems support the process of retrieving either explicit or tacit knowledge that resides within people, artifacts, or organizational entities. These systems can help capture knowledge that resides within or outside organizational boundaries including within consultants, competitors, customers, suppliers, and prior employers of the organization’s new employees.
The earliest mechanisms for knowledge capture dates to the anthropological use of stories – the earliest form of art, education and entertainment. Storytelling is the mechanism by which early civilizations passed on their values and their wisdom from one generation to the next.
The importance of using metaphors and stories as a mechanism for capturing and transferring tacit knowledge is increasingly drawing the attention of organizations.
Technology can also facilitate capturing the knowledge of experts. One type of knowledge capture system is based on the use of concept maps as a knowledge-modeling tool. A concept map is a diagram showing the relationships among concepts.
In the 1960s, Joseph D. Novak (1993) at Cornell University began to study the concept mapping technique. His work was based on the theories of David Ausubel (1968), who stressed the importance of prior knowledge in being able to learn about new concepts. Novak concluded that “Meaningful learning involves the assimilation of new concepts and propositions into existing cognitive structures.”
A concept map is a graphical representation where nodes (points or vertices) represent concepts, and links (arcs or lines) represent the relationships between concepts. The concepts, and sometimes the links, are labelled on the concept map. The links between the concepts can be one-way, two-way, or non-directional. The concepts and the links may be categorized, and the concept map may show temporal or causal relationships between concepts (Plotnick, 1997).
Concept maps provide an effective methodology to organize and structure the concepts representing the expert’s domain knowledge.
Knowledge engineering
Knowledge engineering is a field of artificial intelligence ( AI) that tries to emulate the judgment and behavior of a human expert in a given field.
Knowledge engineering is the technology behind the creation of  expert systems to assist with issues related to their programmed field of knowledge. Expert systems involve a large and expandable  knowledge base integrated with a rules  engine that specifies how to apply information in the  knowledge base to each particular situation. The systems may also incorporate  machine learning so that they can learn from experience in the same way that humans do. Expert systems are used in various fields including healthcare, customer service, financial services, manufacturing and the law.
Using  algorithms to emulate the thought patterns of a subject matter expert, knowledge engineering tries to take on questions and issues as a human expert would. Looking at the structure of a task or decision, knowledge engineering studies how the conclusion is reached. A library of problem-solving methods and a body of collateral knowledge are used to approach the issue or question. The amount of collateral knowledge can be very large. Depending on the task and the knowledge that is drawn on, the virtual expert may assist with  troubleshooting, solving issues, assisting a human or acting as a virtual agent.
Scientists originally attempted knowledge engineering by trying to emulate real experts. Using the virtual expert was supposed to get you the same answer as you would get from a human expert. This approach was called the transfer approach. However, the expertise that a specialist required to answer questions or respond to issues posed to it needed too much collateral knowledge: information that is not central to the given issue but still applied to make judgments.
A surprising amount of collateral knowledge is required to enable analogous reasoning and nonlinear thought. Currently, a modeling approach is used where the same knowledge and process need not necessarily be used to reach the same conclusion for a given question or issue. Eventually, it is expected that knowledge engineering will produce a specialist that surpasses the abilities of its human counterparts.
• How knowledge sharing systems help users share their knowledge, both tacit and explicit
Knowledge Sharing Systems support the process through which explicit or tacit knowledge is communicated to other individuals. These systems are also referred to as knowledge repositories.
The two types of explicit knowledge sharing systems most widely discussed in the KM literature are:
lessons learned and
expertise locator systems.
Systems that support tacit knowledge sharing are those typically utilized by communities of practice.
Corporate Memory (also known as an organizational memory) is made up of the aggregate intellectual assets of an organization. It is the combination of both explicit and tacit knowledge. The loss of Corporate Memory often results from a lack of appropriate technologies for the organization and exchange of documents. Another contributing factor to the loss of corporate memory is the departure of employees because of either turnover or retirement. KM is concerned with developing applications that will prevent the loss of corporate memory.
Knowledge sharing systems are classified according to their attributes
· Incident report databases
· Alert systems
· Best practices databases
· Lessons-learned systems
· Expertise locator systems
Incident report databases are used to disseminate information related to incidents or malfunctions. Incident reports typically describe the incident together with explanations of the incident, although they may not suggest any recommendations.
Alert systems were originally intended to disseminate information about a negative experience that has occurred or is expected to occur. Alert systems could be used to report problems experienced with technology, such as an alert system that issues recalls for consumer products.
Best practices databases describe successful efforts, typically from the reengineering of business processes that could be applicable to organizational processes. Best practices differ from lessons learned in that they capture only succesful events, which may not be dervied from experience.
The goal of lessons-learned systems is to capture and provide lessons that can benefit employees who encounter situations that closely resemble a previous experience in a similar situation. LLS could be pure repositories of lessons or be sometimes intermixed with other sources of information.
Expertise-Locator Systems are knowledge repositories that attempt to organize knowledge by identifying experts who possess specific knowledge. Expertise locator systems are also known as expert directories, expertise directories, skill directories, skills catalogues, white pages or yellow pages.
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