Knowledge management process

Although there is no commonly accepted definition of knowledge management, the following one mentioned in [72] is rather conceivable:

Knowledge management (KM) is a discipline that provides strategy, process, and technology to share and leverage information and expertise that will increase our level of understanding to more effectively solve problems and make decisions.

According to this definition, it is believed that the objective of knowledge management systems is to support creation, transfer, and application of knowledge in organizations [3].

To uncover some assumptions about knowledge that underlie organizational KM process and KMS, several perspectives on knowledge are summarized in [3]. These include

1. the contrast of knowledge with data and information, 2. knowledge as a state of mind,

3. knowledge as an object,

4. knowledge as a process,

5. knowledge as a condition of having access to information, and 6. knowledge as a capability

For the contrast of knowledge with data and information, it is reiterated that data is raw numbers and facts, information is processed data, and knowledge is authenticated information. For knowledge as a state of mind, knowledge is described as “a state or fact of knowing” with knowing being a condition of “understanding gained through experience or study; the sum or range of what has been perceived, discovered, or learned”. For knowledge as an object, it means that knowledge can be viewed as a thing to be stored and manipulated (i.e., an object). For knowledge as a process, it is emphasized that knowledge can be viewed as a process of simultaneously knowing and acting.

For knowledge as a condition of having access to information, organizational knowledge must be organized to facilitate access to and retrieval of content. It is thought of as an extension of the view of knowledge as an object, with a special emphasis on the accessibility of the knowledge objects.

For knowledge as a capability, knowledge can be viewed as a capability with the potential for influencing future action. However, it is also suggested that knowledge is not so much a capability for specific action, but the capacity to use information; learning and experience result in an ability to interpret information and to ascertain what information is necessary in decision making. (See [3] for further references).

These different perspectives of knowledge lead to different emphasis on how knowledge should be managed. Among them, the process view focuses on knowledge flow and the processes of creation, sharing, and distribution of knowledge. This is closely related to the KM life cycle—

creation, capture, organization, and dissemination/sharing[72].

Any KM process starts from the creation of knowledge. The theory of organizational knowledge creation proposed in [66] is concerned with developing new content or replacing existing content within the organization’s tacit and explicit knowledge. According to [3],

rooted in action, experience, and involvement in a specific context, the tacit di-mension of knowledge (henceforth referred to as tacit knowledge) is comprised of both cognitive and technical elements. The cognitive element refers to an individual.s mental models consisting of mental maps, beliefs, paradigms, and viewpoints. The technical component consists of concrete know-how, crafts, and skills that apply to a specific context.

On the other hand, the explicit knowledge is defined as follows [3]:

the explicit dimension of knowledge (henceforth referred to as explicit knowledge) is articulated, codified, and communicated in symbolic form and/or natural language.

Based on such tacit-explicit knowledge classification, four modes of knowledge creation have been identified: socialization, externalization, internalization, and combination [66]. These modes of knowledge creation is explicated in [3] as follows:

The socialization mode refers to conversion of tacit knowledge to new tacit knowl-edge through social interactions and shared experience among organizational members

(e.g., apprenticeship). The combination mode refers to the creation of new explicit knowledge by merging, categorizing, reclassifying, and synthesizing existing explicit knowledge (e.g., literature survey reports). The other two modes involve interactions and conversion between tacit and explicit knowledge. Externalization refers to con-verting tacit knowledge to new explicit knowledge (e.g., articulation of best practices or lessons learned). Internalization refers to creation of new tacit knowledge from explicit knowledge (e.g., the learning and understanding that results from reading or discussion).

However, as pointed out in [91], the term “tacit knowledge” should be replaced by the more appropriate “implicit knowledge”:

Nonaka and Takeuchi put forward the proposition, embodied in the diagram, that

“tacit knowledge” is somehow derived from explicit knowledge and, by other means, is made explicit. However, it is clear, from the analysis above, that implicit knowledge, which is not normally expressed, but may be expressed, is actually intended here. Im-plicit knowledge is that which we take for granted in our actions, and which may be shared by others through common experience or culture. For example, in establishing a production facility in a foreign country, a company knows it needs to acquire local knowledge of “how things are done here”. Such knowledge may not be written down, but is known by people living and working in the culture and is capable of being written down, or otherwise conveyed to those who need to know. The knowledge is implicit in the way people behave towards one another, and towards authority, in that foreign cul-ture, and the appropriate norms of behavior can be taught to the newcomers. Implicit knowledge, in other words, is expressible: tacit knowledge is not, and Nonaka would have saved a great deal of confusion had he chosen the more appropriate term.

Apparently, the decision cases occurred in the past and stored in a data table may embed such a kind of implicit knowledge, and data mining is simply the IT tool to make implicit knowledge explicit. Thus, KDD is a process of knowledge creation from raw data. Once knowledge is cre-ated, its storage, organization, and retrieval, which are also referred as organizational memory, be-come crucial for effective organizational knowledge management. Organizational memory includes knowledge residing in various component forms, including written documentation, structured in-formation stored in electronic databases, codified human knowledge stored in expert systems, and so on [3].

Several technologies to support the KM process are identified in [72]. These technologies include hybrid expert systems; personalization–profiling and customization; taxonomies, search, or knowledge discovery; knowledge metrics; and knowledge visualization. Among them, the core requirement of hybrid expert systems is “to capture the knowledge of experts and translate them into rules and reasoning processes to aid in decision support. Rules may range from simple and rigid to complex and vague” [72]. The objective of our work is to provide a class of logics that can represent such rules and reasoning processes.

The phase next to knowledge organization is knowledge transfer. Knowledge transfer depends on a learning process. Communication process and information flows drive knowledge transfer in organizations. To ease the communication process and reduce the cost of conversion between different forms of knowledge, a uniform style of knowledge representation formalism is expected.

Since the logics proposed in this thesis are all the same style—they are all based on extensions of the decision logic, we can achieve the purpose of easy communication between different units or organizations.

The last phase of knowledge management is the application of knowledge. It is emphasized in [3] that the source of competitive advantage of KM resides in the application of the knowledge rather than in the knowledge itself. There are three primary mechanisms for the integration of knowledge to create organizational capability: directives, organizational routines, and self-contained task teams[3]. An important problem of knowledge application may be “deciding upon the rules and routines to apply to a problem, given that over time, the organization has learned and codified a large number of rules and routines, so that choosing which rules to activate for a specific choice making scenario is itself problematic”[3]. The technology that can help in this phase is the reasoning power of decision-support systems. As mentioned in Chapter 1, our logic-based representation facilitates the easy integration of the knowledge bases with inference engines of the decision-support systems.

The knowledge management process is summarized in Figure 2.2, where knowledge represen-tation is highlighted with red color to reiterate the main theme of our work.

Knowledge creation

• Data mining

• Text mining

• Brainstorming etc.

Knowledge representation

• Organizational memory

• Document

• Codified knowledge (logic, rules, expert systems, etc)

Knowledge transfer

• Communication

• Formal and informal

• Personal and impersonal

Knowledge application

• Rule activation

• Easy access and maintenance

• Expert systems

Figure 2.2: The knowledge management life cycle