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An approach to expert recommendation based on fuzzy linguistic method and fuzzy text classification in knowledge management systems

机译:知识管理系统中基于模糊语言方法和模糊文本分类的专家推荐方法

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摘要

Since organizational tacit knowledge such as know-how and experiences usually resides in the owner's brain, consulting the expert is an effective and efficient way to utilize this type of knowledge. However, users are no longer able to effectively find the appropriate experts in the knowledge management system due to the complexity and diversity of the expertise and the knowledge needs. In this paper, an approach to expert recommendation is proposed to assist the user to find the required experts. The method adopts the fuzzy linguistic method to construct the expert profile, that is, to model expert's expertise. In addition, the fuzzy text classifier is used to get the relevant degree of the document to each knowledge area when the document is registered, which is the base of the following user profile construction. Then, the user profile consisting of the time and the relevance factors of the rated documents is constructed to derive the overall knowledge needs level of the user. Consequently, the expert that fulfills the knowledge needs most is recommended based on the similarity between the derived expert profile and the user profile. The developed prototype system, "knowledge management system in aircraft industry company", is introduced and the experimental results show the proposed approach is feasible and effective.
机译:由于诸如技术和经验之类的组织隐性知识通常存在于所有者的大脑中,因此咨询专家是利用此类知识的有效途径。但是,由于专业知识和知识需求的复杂性和多样性,用户不再能够有效地在知识管理系统中找到合适的专家。在本文中,提出了专家推荐的方法,以帮助用户找到所需的专家。该方法采用模糊语言学方法来构建专家档案,即对专家的专业知识进行建模。另外,当文档被注册时,模糊文本分类器用于获得文档到每个知识区域的相关程度,这是随后的用户配置文件构建的基础。然后,构建由时间和相关文档的相关性因子组成的用户资料,以得出用户的整体知识需求水平。因此,基于派生的专家档案和用户档案之间的相似性,推荐最满足知识需求的专家。介绍了开发的原型系统“飞机工业公司的知识管理系统”,实验结果表明该方法是可行和有效的。

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