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An Approach to Tacit Knowledge Classification in a Manufacturing Company

机译：一种在制造公司中默认知识分类的方法

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摘要

This article attempts to classify the tacit knowledge acquired using the example of the research and development (R&D) department of a manufacturing company. Based on studies in the literature and direct interviews in the company analysed, the authors' own model for classifying the tacit knowledge in an R&D department was proposed. The description of this model has been divided into two parts. In the first part, viz., the classification of knowledge, through three planes: (1) selection of algorithm inputs and the grouping of knowledge accumulated in an enterprise; (2) algorithm activity, that is, the use of algorithms based on clustering them, for calculations; (3) interpretation of results. The Bayesian Network was used for this purpose, which was modelled on the defined relationships between representing of tacit knowledge. Then, on the basis of the case study, the classification of knowledge was prepared according to: (1) definition of knowledge in the R&D department and its modelling, (2) implementation of a suitable number of training sets, (3) verification of the knowledge base, that is, declaration of the value of the knowledge observed, followed by (4) assignment of the probability of returning to the node of network clusters containing interpretations of business benefits.
机译：本文试图使用制造公司的研发（R＆amp; d）部门的示例进行分类所获得的默认知识。基于本公司文献和直接访谈的研究，提出了对R＆amp中的默契知识进行分类的作者自己的模型。该模型的描述已分为两部分。在第一部分，viz，知识的分类，通过三个飞机：（1）选择算法的输入和企业积累的知识分组; （2）算法活动，即基于聚类的算法，计算; （3）解释结果。贝叶斯网络用于此目的，该目的是在代表默认知识之间的定义关系上建模的。然后，在案例研究的基础上，根据的知识分类是根据的：（1）R＆amp; D部门及其建模的知识定义，（2）实施合适数量的培训套装，（3）知识库的验证，即观察到知识值的声明，其次是（4）返回返回包含商业福利的解释的网络集群节点的概率。

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