What are the Most Important Evolutionary Innovations?

Most existing knowledge management system (KMS) models of traditional knowledge management (KM) theory lack strong support for the process of knowledge evolution, which constrains the improvement of the overall order of the system. Under the premise that the quality of the enterprise knowledge base cannot be guaranteed, the efficiency and effectiveness of enterprise KM implementation will be directly damaged. Establishing the concept of knowledge evolution and establishing a sound knowledge evolution mechanism to ensure the effectiveness of corporate knowledge and the vitality of the knowledge base are the basic guarantees for enterprises to effectively implement KM.

Knowledge evolution

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Knowledge evolution clarifies the significance of knowledge evolution in the implementation of enterprise KM, and has a fundamental role in correctly establishing the goal of enterprise KM knowledge evolution and establishing an effective knowledge evolution mechanism under this guidance.

The KMS knowledge evolution subsystem (KES) is used to effectively support this link. KES knowledge evolution is based on the principle of knowledge life cycle. It continuously reviews and evaluates the enterprise knowledge base (including coding knowledge and non-coding knowledge), and timely updates and eliminates the knowledge that is inactive or inactive to ensure that the enterprise The quality of intellectual assets and the effectiveness of knowledge management systems in solving problems.

Knowledge evolution ideas and processes for implementing knowledge evolution

From the perspective of macro knowledge, the life cycle of knowledge is generally shortened. From the perspective of micro knowledge units, different types and contents of knowledge have different life cycles. KES knowledge evolution draws on Darwin's theory of biological evolution and also has a "natural selection" function, the difference is that it requires subjective judgment and participation of the subject of knowledge.

2 Knowledge evolution is achieved in 2 stages

First, the activity of knowledge units is measured. The concept of "knowledge activity" is used to realize the image description of the vitality of the knowledge unit, and it is represented by the interaction between knowledge object and knowledge subject-knowledge value. The greater the value of knowledge, the higher the activity of the knowledge unit.

Second, the knowledge unit whose knowledge activity reaches or falls below a given evolution threshold is treated according to its specific knowledge value, and corresponding evolutionary processing is performed. This includes 2 types of situations:

For knowledge units whose knowledge value is lower than a given evolution threshold but higher than the elimination threshold, the characteristics between the current knowledge environment and the original knowledge environment (the knowledge environment when the knowledge unit was acquired or last updated / improved) are targeted. The differences are updated and improved accordingly to enhance the knowledge's activity and thus extend its life cycle. The evolution threshold and elimination threshold of the knowledge unit are given by domain experts according to the characteristics of the knowledge application domain.

For the knowledge unit whose knowledge value reaches or falls below the elimination threshold, it is decisively discarded or put into a "dormant" state. Knowledge value has a strong environmental dependency. Some knowledge units whose activity reaches or falls below the elimination threshold in the current application environment may still regain or enhance their activity when the environmental conditions change at some point in the future. Knowledge "hibernation" is based on this level of consideration. Some knowledge units whose knowledge value reaches or falls below the elimination threshold are transferred from the active state to the "hibernation" state, which saves maintenance costs and improves system performance. Meet and activate it instantly.

Like biological evolution, KES knowledge evolution also has individual evolution (knowledge unit level) and group evolution (knowledge level). The cumulative effect of mutations in individual evolution will ultimately guide and contribute to the effects of population evolution. When the level of knowledge units maintains a continuous and effective level of evolution, the quality and effectiveness of the entire knowledge base will steadily improve. According to the magnitude of evolution, knowledge evolution is divided into applied evolution and periodic evolution. Applied knowledge evolution is to update and improve the knowledge unit in response to changes in the knowledge application environment and the feedback of application effects during the application process of the knowledge unit. Obviously, this is the process of knowledge adaptation, modification and system learning in the process of knowledge application. Periodic knowledge evolution refers to the evaluation of the value of some or all of the knowledge in the knowledge base at every time period T (depending on the characteristics of the field of knowledge application) and the corresponding evolutionary processing.