基于 IG-NRS 与ICK 双向压缩的 KMS 自学习案例知识匹配.

Case knowledge matching can effectively alleviate the knowledge overload problem and ensure the level of knowledge application. Aiming at the redundancy problem of self-learning case knowledge matching in knowledge management systems, this paper proposed a bidirectional compression method based on IG-NRS and ICK. The method firstly designed an improved model of NRS, called IG-NRS. Accordingly, it approximated the set of case knowledge attributes to achieve the vertical compression of the neighborhood decision system. On this basis, it realized horizontal compression by introducing spectral clustering discrimination and eliminating the inconsistent case knowledge. And then it determined the knowledge matching results by locking the target case knowledge clusters and the most similar case knowledge through the similarity of knowledge views. Experimental results on several UCI datasets show that this method can effectively reduce the redundancy of self-learning case knowledge in knowledge management systems and achieve higher knowledge matching efficiency and effectiveness. [ABSTRACT FROM AUTHOR]